Informatic tools for diagnosis in dentistry. A compilation review

Informatic tools for diagnosis in dentistry. A compilation review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Informatic tools for diagnosis in dentistry. A compilation review Alain Manuel Chaple Gil This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5976843/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The objective of this study was to compile the computer tools available in the scientific literature aimed at helping the diagnosis in dentistry. A scoping review was conducted using PubMed , Scopus , and Web of Science . Were include articles that reported the usefulness of a computer/technological tool that helps diagnosis in dental practice, published in the last 20 years in English and Spanish. Online tool Rayyan® was used to establish homogeneity in the review of the authors and centralize the results. In total, 12648 records were retrieved from the databases. After decantation, 39 reports described 36 computer tools used to help for diagnosis in dentistry. More informatic tools related to "Restorative Dentistry’ have been developed than the rest of the specialties 14 (40%). Python was the predominant programming language, 83.3% of the tools were validated, and 27.8% were free. Informatics tools in dentistry enhance the diagnosis and treatment planning. However, a robust regulatory framework is required for validation prior to clinical implementation. Continuous training of dental professionals using these technologies is crucial to maximize their benefits and ensure optimal patient care. More research is needed to explore the potential of informatics applications in dentistry, their integration into existing health systems, and their accessibility in resource-limited areas. Dentistry Medical Informatics (MeSH): Clinical Informatics Medical Informatics Applications Dental Informatics Medical Informatics Computing Oral Diagnosis Figures Figure 1 INTRODUCTION Dentistry, like other medical disciplines, has undergone a significant digital transformation over the past few decades. The incorporation of digital technologies has improved the efficiency, precision, and scope of dental procedures, positively affecting both professionals and patients. However, the availability of a wide range of digital tools for diagnosis in dentistry poses a significant challenge due to the mercantilist stocks of the most widely used. ( 1 , 2 ) A health informatics tool is a system or platform designed to acquire, process, interpret, and use healthcare data efficiently. ( 3 ) These tools aim to improve patient safety by identifying the hazards and risks associated with health information technology. ( 4 ) They play a crucial role in improving health service delivery by ensuring that the information reaches the right person at the right time. Such tools help in decision making, access to information, disease prevention, and improved communication, allowing patients and their families to manage their health effectively. These tools span various technologies, such as wearable devices, passive monitoring tools, and smart homes, providing personalized interventions based on patient-generated health data. ( 5 ) Computer tools are increasingly used in dental practice for diagnostic purposes. These tools use intelligent systems and machine learning algorithms to analyze dental images and detect various dental conditions such as periodontal disease, alveolar bone loss, and dental pathologies. The use of deep convolutional neural networks (CNN) has yielded promising results in accurately detecting and classifying these conditions based on radiographic images. ( 6 ) In addition, machine vision algorithms applied to high-resolution images can help diagnose periapical lesions and plan endodontic treatment. ( 7 ) Computerized dental office systems also contribute to improved efficiency and diagnostic tasks, such as early detection of cavities, through image processing software and digital X-rays. ( 8 ) In addition, computer systems that implement neural networks can process dental images to detect and diagnose pathological conditions, providing a graphical user interface for visualization and correlation with dental medical records. ( 9 ) These informatics tools offer valuable support for dental diagnosis, treatment planning, and patient education, and improve the overall quality of dental care. ( 6 , 8 , 10 ) The proliferation of computer tools for diagnosis in dentistry presents opportunities, but also generates a series of questions and difficulties for dentists. ( 11 , 12 ) There is a wide variety of these, from radiographic image analysis software to treatment design and planning systems, to augmented reality applications and intraoral scanners. This diversity makes it difficult to identify the most appropriate tools for each case. ( 12 ) The identification and selection of appropriate informatics tools for diagnosis in dentistry is crucial to improve diagnostic accuracy, as they can provide additional and more accurate information for diagnosis, allowing for better treatment planning. ( 13 ) They optimize workflow by being able to streamline diagnostic processes, improving efficiency and productivity and can help visualize, explain problems and treatment options to the patient, improving understanding and decision-making. ( 14 ) Purpose Definition The purpose of this review was to identify the informatics tools reported in the scientific literature that have been tested to aid diagnosis in dental practice. Based on this approach, the research problem is shown through the following question: What are the computer tools reported by science that could help establish diagnoses in dental practice? OBJECTIVE The objective of this study was to compile the computer tools available in the scientific literature aimed at helping the diagnosis in dentistry. METHODOLOGY A scoping review was conducted between January and April 2024. The PubMed , Scopus , and Web of Science (WOS) databases were used for this development. The reporting guidelines for literature reviews described in the PRISMA guidelines were followed. Inclusion criteria This manuscript includes original research-type articles, articles that report the usefulness of a computer/technological tool that helps diagnosis in dental practice, and articles published in the last 20 years (period 2004–2024) and written in English and Spanish. Exclusion Criteria Review articles of any kind (narrative, systematic, and meta-analyses), letters, case presentations, books, theses, preprints , articles describing computer tools for diagnosis unrelated to stomatology, articles describing generic software for the visualization of images taken with CAD-CAM or similar equipment whose validations are frankly demonstrated, articles written in languages other than English and Spanish and Articles that did not specify a name for the tool they report were excluded. Search strategy A search was carried out using advanced formulations on the three platforms described in Table 1 . Entered keywords combined with wildcards and Boolean operators were displayed. Table 1 Formulations of the search by database used Database Formulation Pubmed ((application[Title/Abstract]) OR (program[Title/Abstract]) OR (tool[Title/Abstract]) OR (informatic[Title/Abstract]) OR (computer[Title/Abstract]) OR (digital[Title/Abstract]) OR (apk[Title/Abstract]) OR (system[Title/Abstract]) OR (automatic[Title/Abstract])) AND ((dental[Title/Abstract]) OR (dentistry[Title/Abstract])) AND ((diagnosis[Title/Abstract]) OR (diagnoses[Title/Abstract])) Scopus (TITLE-ABS-KEY (program*) OR TITLE-ABS-KEY (application*) OR TITLE-ABS-KEY (tool*) OR TITLE-ABS-KEY (comput*) TITLE-ABS-KEY (informatic*) OR TITLE-ABS-KEY (apk) OR TITLE-ABS-KEY (system*) OR TITLE-ABS-KEY (digital*) OR TITLE-ABS-KEY (automa*)) AND (TITLE-ABS-KEY (dent*)) AND (TITLE-ABS-KEY (diagnos*)) WOS (TI=(program*) OR TI=(application*) OR TI=(tool*) OR TI=(comput*) OR TI=(informatic*) OR TI=(apk) OR TI=(system*) OR TI=(digital*) OR TI=(automa*)) AND (TI=(dent*)) AND (TI=(diagn*)) Methodology for the selection of articles In the databases where the searches were performed, automated filters were applied to discard articles that were not of the type described in the inclusion criteria and to establish the publication time range. Records extracted from the databases were exported to Clartivates Analytics EndNote 21. In this same system, duplicate articles that did not meet the eligibility criteria were decanted at first glance. Calibration was performed among the authors to evaluate the articles to be selected. The degree of coincidence of the evaluations made by the reviewers was determined using Orwin's method of 1994, and a Kappa statistic was performed to measure the agreement among the reviewers who would make simple decisions about inclusion/exclusion. Kappa values between 0.40 and 0.59 were considered to reflect acceptable agreement, 0.60 to 0.74 to be an adequate agreement, and 0.75 or more to reflect excellent agreement. The online tool Rayyan® was used to establish homogeneity in the authors’ review on a single online platform where they had access and could centralize the results. When the two reviewers did not agree to the decision to include or exclude a registry, a third party intervened to make the final decision. First, duplicate reports written in languages other than English and Spanish were discarded, applying obvious inclusion/exclusion criteria such as type of document and year of publication. Subsequently, articles that did not meet the criteria were discarded based on their titles and abstracts. Reports with titles and abstracts that were doubtful of being discarded or were not discarded were read to determine this. Once those articles that did not meet the criteria were discarded, the included articles were read in detail to extract the necessary data related to the research variables. Variables The following variables were extracted from each article included in the study: name and surname of the first author, year of publication, Article Title, Journal in which it was published, citation details (including volume, number, and pages), the URL or DOI of the document (preferably the DOI), name of the tool described, country where it was developed, Tool Platform (this refers to the programming language in which the tool was developed), environment (Windows, Linux, Unix, Android, iOS, or other), dental specialty to which it pays, a brief description of the usefulness of the tool, tool validation (dichotomous yes or no), open source (dichotomous yes or no), and usage charge (dichotomous yes or no). For the Payment model, full payment is considered: users pay a one-time price to access the app and all its functionalities, a recurring fee (monthly, yearly, etc.) to access the application and its functionalities, or a license is purchased to use the application for a certain period or in perpetuity. Freemium : The app can be used for free, but it offers optional in-app purchases to unlock additional features, exclusive content, or to remove advertising. In addition, it was included that they were free versions with basic functionalities and paid versions with more advanced functionalities. Trial model : This offers a free trial period during which all features can be accessed. After the trial period, the user must pay to continue using the app and Free Model : Completely free, without any payment or restrictions. In addition, the URL through which the user manual and/or instructions for the installation and use of each tool can be downloaded was included, if available. Data management The records of the variable data extracted from the included articles were captured in a Microsoft® Excel® spreadsheet to later favor the analysis and preparation of tables and figures for better understanding. In cases the articles were not sufficiently explicit, the authors were contacted via email for more information. Respondents increased the scope of the information for this research. Articles by authors who did not respond within a reasonable period of time and the information in the articles that did not appear sufficiently for data collection were excluded. The database of the articles included in the study with all the details collected according to the variables is located in the Mendeley Data repository ( https://doi.org/10.17632/yx9jts8mv7.1 ), which advocates for the reproducibility, sharing, and systematicity of data according to the principles of Open Science. Ethical aspects The protocol for this study was approved by the Scientific Council of the "Ana Betancourt" Dental Clinic in Havana, Cuba. RESULTS The calibration of the authors who reviewed the articles included in the study was excellent in all cases. The search was conducted on January 28 2024. In the primary search, 3397 records were established in Pubmed, 7563 in Scopus , and 221 in WOS, for a total of 12648. After applying the filter of publication time (2004–2024) and the type of article, there were 46 results in PubMed (only for clinical trials and randomized controlled trial articles ), 5678 in Scopus , and 162 in WOS, resulting in 5886 records. After removing duplicates, 4159 articles remained, and applying the inclusion criteria of article type 3937 and excluding articles in languages other than English and Spanish, 3655 entries remained. (Fig. 1 ) When examining the titles and abstracts of the remaining articles, 3287 articles were excluded, leaving 368 reports that were downloaded in full text and excluded five for not exactly describing a telematic tool for diagnosis in dentistry, 95 were excluded because they lacked the necessary details for the adequate collection of information, another 11 were discarded for loss of access through URLs, and 257 were not strictly complying with the criteria inclusion/exclusion. At the end of the process, 39 reports described 36 computer tools used for diagnosis in dentistry. (Fig. 1 ) Table 2 shows the list of articles included in the study with a predominance of articles published in the International Journal of Environmental Research and Public Health” ( 15 , 16 , 17 , 18 ) with 4 reports for 10.3% of the total, followed by the "Diagnostics” ( 19 , 20 , 21 ) with three (7.7%) reports. Only O'Toole ( 22 , 23 ) matched two (5.1%) articles as the first author. The years of publication of the articles ranged from 2015 to 2023, with 2022 predominating ( 17 , 18 , 21 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ) and 10 (25.6%) reports. Table 2 List of articles included in the study with the first author's surname, year of publication, title, and journal in which they were published. # 1st Author Year Article Title Journal 1 Abd El Galil ( 31 ) 2021 Evaluation of two computer-aided design software on the adaptation of digitally constructed maxillary complete denture Journal of Indian Prosthodontic Society 2 Abuabara ( 32 ) 2023 Evaluation of Endo 10 mobile application as diagnostic tool in endodontics J Clin Exp Dent 3 Alalharith ( 15 ) 2020 A deep learning-based approach for the detection of early signs of gingivitis in orthodontic patients using faster region-based convolutional neural networks International Journal of Environmental Research and Public Health 4 Altukroni ( 33 ) 2023 Detection of the pathological exposure of pulp using an artificial intelligence tool: a multicentric study over periapical radiographs BMC Oral Health 5 Amorim ( 24 ) 2022 Smartphone and computer cephalometric analysis: A trueness and precision study Revista Portuguesa de Estomatologia, Medicina Dentaria e Cirurgia Maxilofacial 6 Anacleto ( 34 ) 2019 Superimposition of 3d maxillary digital models using open-source software Dental Press Journal of Orthodontics 7 Barreto ( 35 ) 2016 Reliability of digital orthodontic setups Angle Orthodontist 8 Baydar ( 19 ) 2023 The U-Net Approaches to Evaluation of Dental Bite-Wing Radiographs: An Artificial Intelligence Stud Diagnostics 9 Bonfanti-Gris ( 25 ) 2022 Evaluation of an Artificial Intelligence web-based software to detect and classify dental structures and treatments in panoramic radiographs Journal of Dentistry 10 Cavalcante ( 16 ) 2020 Computing and oral health: Mobile solution for collecting, data analysis, managing and reproducing epidemiological research in population groups International Journal of Environmental Research and Public Health 11 Cui ( 36 ) 2021 TSegNet: An efficient and accurate tooth segmentation network on 3D dental model Medical Image Analysis 12 Dayı ( 20 ) 2023 A Novel Deep Learning-Based Approach for Segmentation of Different Type Caries Lesions on Panoramic Radiographs Diagnostics 13 De Angelis ( 17 ) 2022 Artificial Intelligence: A New Diagnostic Software in Dentistry: A Preliminary Performance Diagnostic Study International Journal of Environmental Research and Public Health 14 Di Fede ( 37 ) 2023 Doctoral: A smartphone-based decision support tool for the early detection of oral potentially malignant disorders Digital Health 15 do Vale Voigt ( 38 ) 2020 DSDapp use for multidisciplinary esthetic planning Journal of Esthetic and Restorative Dentistry 16 Estai ( 39 ) 2017 End-user acceptance of a cloud-based teledentistry system and Android phone app for remote screening for oral diseases Journal of Telemedicine and Telecare 17 Fatima ( 40 ) 2023 Deep Learning-Based Multiclass Instance Segmentation for Dental Lesion Detection Healthcare (Switzerland) 18 Fazio ( 18 ) 2022 LinguAPP: An m-Health Application for Teledentistry Diagnostics International Journal of Environmental Research and Public Health 19 Gullberg ( 26 ) 2022 The challenge of applying digital image processing software on intraoral radiographs for osteoporosis risk assessment Dentomaxillofacial Radiology 20 Haron ( 41 ) 2020 M-Health for Early Detection of Oral Cancer in Low- and Middle-Income Countries Telemedicine and e-Health 21 Jiang ( 42 ) 2021 RDFNet: A Fast Caries Detection Method Incorporating Transformer Mechanism Computational and Mathematical Methods in Medicine 22 Johannes ( 43 ) 2023 Evaluation of AI Model for Cephalometric Landmark Classification (TG Dental) Journal of Medical Systems 23 Kapoor ( 27 ) 2022 Development, testing, and feasibility of a customized mobile application for obstructive sleep apnea (OSA) risk assessment: A hospital-based pilot study Journal of Oral Biology and Craniofacial Research 24 Kim ( 44 ) 2019 DeNTNet: Deep Neural Transfer Network for the detection of periodontal bone loss using panoramic dental radiographs Scientific Reports 25 Liu ( 45 ) 2020 A Smart Dental Health-IoT Platform Based on Intelligent Hardware, Deep Learning, and Mobile Terminal IEEE Journal of Biomedical and Health Informatics 26 Livas ( 46 ) 2019 Concurrent validity and reliability of cephalometric analysis using smartphone apps and computer software Angle Orthodontist 27 Lo Giudice ( 47 ) 2021 A new software architecture proposal for an evidence-based decision support system in dentistry Minerva Dental and Oral Science 28 Mazzoli ( 48 ) 2017 Use of MIMICS® software in three-dimensional cephalometric evaluation of soft tissues of the face Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization 29 Muhammed Sunnetci ( 28 ) 2022 Periodontal bone loss detection based on hybrid deep learning and machine learning models with a user-friendly application Biomedical Signal Processing and Control 30 Najmuddin ( 49 ) 2018 Logicon: A third eye for caries detection Journal of Indian Academy of Oral Medicine and Radiology 31 Orhan ( 50 ) 2023 Determining the reliability of diagnosis and treatment using artificial intelligence software with panoramic radiographs Imaging Science in Dentistry 32 O'Toole ( 23 ) 2019 Investigation into the validity of WearCompare, a purpose-built software to quantify erosive tooth wear progression Dental Materials 33 O'Toole ( 22 ) 2020 Influence of scanner precision and analysis software in quantifying three-dimensional intraoral changes: Two-factor factorial experimental design Journal of Medical Internet Research 34 Preus ( 51 ) 2015 A new digital tool for radiographic bone level measurements in longitudinal studies BMC Oral Health 35 Reyes Salgado ( 29 ) 2022 Design of Open Code Software to Downs and Steiner Lateral Cephalometric Analysis with Tracing Landmarks Digital 36 Roehl ( 30 ) 2022 Tooth Wear Evaluation System (TWES) 2.0—Reliability of diagnosis with and without computer-assisted evaluation Journal of Oral Rehabilitation 37 Santeiro-Hermida ( 52 ) 2023 Validation Analysis of Panoramic Dental Application (PDApp) Software as a Tool for Predicting Third Molar Eruption Based on Panoramic Radiograph Images Applied Sciences (Switzerland) 38 Sayar ( 53 ) 2017 Manual tracing versus smartphone application (app) tracing: a comparative study Acta Odontologica Scandinavica 39 Zadrożny ( 21 ) 2022 Artificial Intelligence Application in Assessment of Panoramic Radiographs Diagnostics n = 39 Source : Developed by authors Table 3 shows the relationship of computer tools described by the articles with the country where they were developed, year of creation, programming language, platforms on which they can be used, the specialty with which they are related, whether they were validated, open source, free, and the payment model to which they are subject. “CephNinja, ” ( 46 , 53 ) “Diagnocat, ” ( 21 , 50 ) “OneCeph” ( 24 , 46 ) y “WearCompare” ( 22 , 23 ) were the recurrent tools and that 2 (5.1%) articles each described their use. Only six ( 18 , 26 , 31 , 36 , 41 , 48 ) (15.4%) validation processes were not referred to in the articles, and the rest were validated. The United States was the most representative country for the development of five (14.3%) of these computer tools (3D Slicer, CephNinja, Diagnocat, Geomagic, and Logicon). (Table 3 ) More informatic tools related to "Restorative Dentistry" have been developed than the rest of the specialties of the dental sciences with 14 (40%), followed by the tools related to "Orthodontics" 9 (25.7%) and those useful for "Oral Surgery" 5 (13.9%). (Tables 3 and 4 ) The most predominant programming language for tool development was Python, with 7 (19.4%), followed by C++, which was used for the development of 5 (13.8%). (Table 3 ) Thirty (83.3%) tools were validated, and six (16.7%) were not specified due to lack of information in the articles. A total of six (16.7%) open-code tools were described, another 16 (44.4%) were not, and of the rest, no records of this data were found in the articles. In addition, 10 (27.8%) tools were declared free, 10 (27.8%) were not, and the rest were not provided in the articles. (Table 3 ) Table 3 List of informatic tools extracted from articles with general data on development and availability. # Tool name Country Year Programming language Platforms Specialty Validated Open code Free Payment model 1 3D Slicer ( 34 ) USA 1999 C++ Windows, macOS y Linux. O Yes Yes Yes FM 2 Apox ( 17 ) Netherlands 2022 NR NR RD Yes NR NR NR 3 Artifical_Intelligence_Toolbox ( 28 ) Turkey NR MATLAB 2020b - GUI Windows, Android P Yes NR NR NR 4 Boneprox ( 26 ) Sweden 2021 NR Windows RD NR NR NR NR 5 Cephalopoint ( 29 ) Mexico 2022 Octave Android, iOS, Linux Windows, Unix O Yes Yes Yes FM 6 CephNinja ( 46 , 53 ) USA - India NR NR Android, iOS O Yes NR NR NR 7 CranioCatch ( 19 ) Turkey 2022 Python (PyTorch) Android, iOS, Linux Windows, Unix RD Yes No No FPM 8 Decision Support System ( 47 ) Italy NR Visual C# NR RD Yes NR NR NR 9 DCDNet ( 20 ) Turkey 2022 Python (TensorFlow) NR RD Yes NR NR NR 10 Denti.Ai ( 25 ) Canada 2017 Faster R-CNN, VGG-16 Android, iOS, Linux Windows, Unix RD Yes No No FPM 11 DeNTNet ( 44 ) South Korea NR NR NR P Yes NR NR NR 12 Diagnocat ( 21 , 50 ) USA 2017 Python Any Web Navigator OS Yes No No FPM 13 DoctOral ( 37 ) Italy 2014 NR Android, iOS OS Yes No Yes FM 14 DSDapp ( 38 ) Brazil 2018 NR iOS RD Yes No No FPM 15 Endo 10 ( 32 ) Brazil 2021 NR Android, iOS E Yes NR Yes FM 16 Faster R-CNN ( 15 ) Saudi Arabia 2020 Python (TensorFlow) Linux, Windows, macOS P Yes NR NR NR 17 Geomagic ( 31 ) USA 1996 NR Windows OR NR No No TM 18 iHome DentalHealth-IoT ( 45 ) China 2018 NR Android, iOS RD Yes No NR NR 19 ImageJ (Plugin) ( 51 ) Norway 2015 NR Windows, macOS, Linux P Yes Yes Yes F 20 LinguAPP ( 18 ) Italy 2020 JavaScript Android, iOS OS NR No Yes FM 21 Logicon ( 49 ) USA 2002 NR NR RD Yes No No NR 22 MSC ( 33 ) Saudi Arabia 2021 Python - Yolov5-x Windows, Linux, macOS. RD Yes NR NR NR 23 Mask-RCNN ( 40 ) NR NR Python (Tensorflow, Keras) NR E Yes NR NR NR 24 MeMoSA ( 41 ) Malaysia 2017 React Native Android OS NR NR NR NR 25 MIMICS ( 48 ) Belgium NR C++ Windows, Linux O NR No No FPM 26 NutriOdonto ( 16 ) Brazil 2018 NR Android RD Yes Yes Yes FM 27 OneCeph ( 24 , 46 ) India NR NR Android O Yes No Yes NR 28 OrthoAnalyzer ( 35 ) Denmark 2014 NR NR O Yes No No FPM 29 OSA-Risk Assessment Tool ( 27 ) India 2020 HTML, PHP Any Web Navigator O Yes Yes Yes FM 30 PDApp ( 52 ) Spain 2022 C++ Linux, Windows OS Yes No NR NR 31 RDFNet ( 42 ) China 2021 Python (Pytorch) Windows, Linux, MacOS RD Yes NR NR NR 32 Remote-I ( 39 ) Australia 2015 NR Android RD Yes NR NR NR 33 TG Dental ( 43 ) NR NR NR NR O Yes NR NR NR 34 TWES ( 30 ) Germany 2020 MariaDB Windows, macOS OR Yes No No FPM 35 TSegNet ( 36 ) Hong Kong 2019 NR iOS O NR No No FPM 36 WearCompare ( 22 , 23 ) United Kingdom 2019 C++ Windows, Linux RD Yes Yes Yes FM n = 36 Source : Developed by authors - Caption : O (Orthodontics), OS (Oral surgery), OR (Oral Rehabilitation), RD (Restorative dentistry), P (Periodontics), E (Endodontics), NR (Not referred), FPM (Full Paid Model), FM (Free Model), F (Freemium), TM (Test Model) Table 4 shows the grouping of the tools according to the specialties in which they are useful for diagnosis to facilitate consultation according to the affinity of interested readers. Table 4 Distribución de herramientas informáticas según especialidad en la que ayudan al diagnóstico. RESTORATIVE DENTISTRY ORTHODONTICS ORAL SURGERY PERIODONTICS PROSTHODONTICS ENDODONTICS Apox 3D Slicer Diagnocat Artifical_Intelligence_Toolbox Geomagic Endo 10 Boneprox Cephalopoint DoctOral DeNTNet TWES Mask-RCNN CranioCatch CephNinja LinguAPP Faster R-CNN Decision Support System MIMICS MeMoSA ImageJ (Plugin) DCDNet OneCeph PDApp Denti.Ai OrthoAnalyzer DSDapp OSA-Risk Assessment Tool iHome DentalHealth-IoT TG Dental Logicon TSegNet MSC NutriOdonto RDFNet Remote-I WearCompare n = 36 DISCUSSION The search engines used to carry out this research were Scopus, Web of Science, and PubMed, which are essential databases for systematic reviews owing to their wide coverage and unique characteristics. Scopus, as highlighted by Schwager and Schalk ( 54 ) , demonstrated high sensitivity in retrieving relevant articles, while Web of Science, as shown in Kumpulainen and Seppänen ( 55 ) , efficiently identified additional studies by searching for citations. PubMed, which is renowned for its extensive coverage of the biomedical literature, is crucial for health-related systematic reviews. The use of these databases ensures a thorough search process, as highlighted in the work of Alfandaria and Taylor ( 56 ) , who found that replicability varied from platform to platform. Each database contributes to minimizing bias and maximizing the retrieval of relevant studies, aligning with best practices for systematic literature searches. ( 57 ) Based on these approaches, the reason why these databases were chosen for the search of articles in this research is shown. The tools described in the results have great potential to improve diagnosis, treatment planning, and information management in the field of dentistry and can be grouped to provide a complete overview and analyze them in detail according to their specialization and functions. Among the tools for cephalometric analysis and diagnosis, Cephalopoint ( 29 ) and OneCeph ( 24 , 46 ) offer digital cephalometric analysis, facilitating the diagnosis and planning of orthodontic treatments. CephNinja ( 46 , 53 ) allows digital cephalometric plotting on mobile devices, further streamlining the process. In addition, MIMICS ( 48 ) provides three-dimensional cephalometric evaluation of facial soft tissues, which is crucial for assessing facial harmony and orthodontic planning. Studies by da Fonseca Reis et al. ( 58 ) and Prince et al. ( 59 ) argue that computer tools that enable cephalometric tracking offer several advantages in orthodontic diagnosis and treatment planning. These tools improve the accuracy, reproducibility, and efficiency of cephalometric image analyses. On the other hand, they eliminate human errors associated with manual tracking, allow automatic detection of landmarks, and offer a high level of agreement with manual methods. ( 60 ) In addition, according to Reyes Salgado ( 29 ) computerized cephalometric tracing methods are more reliable and consistent than manual tracing, ensuring accurate measurements for treatment proposals and diagnostic hypotheses. For the diagnosis of cavities and other dental diseases, we found Logicon, ( 49 ) RDFNet, ( 42 ) and DCDNet ( 20 ) , which specialize in detecting cavities in dental imaging, while Mask-RCNN ( 40 ) focuses on the detection and classification of periapical lesions. In addition, Denti.Ai ( 25 ) y Diagnocat ( 21 , 50 ) uses AI to identify a variety of dental pathologies, with Denti.Ai ( 25 ) also focusing on implants and crowns. On the other hand, TG Dental ( 43 ) classifies malocclusions without manual identification of landmarks using AI to speed up diagnosis and planning. Computer tools for diagnosing tooth decay from images offer objective verification, aid in doctor-patient communication, teledentistry, and potentially improve diagnostic accuracy and efficiency in the detection of oral diseases. ( 61 ) Studies such as Tareq et al. ( 62 ) argue that these applications make it possible to predict dental cavitations from non-standardized photographs with reasonable clinical accuracy, improving access to oral health care in resource-limited areas. In addition, the use of deep learning in panoramic images makes it possible to accurately detect various tooth-related diseases in real-time, helping to plan treatment in time and reducing the risk of misdiagnosis. ( 63 ) To support clinical decisions, we have a Decision Support System" ( 47 ) that standardizes the decision-making process in dentistry and offers evidence-based therapeutic options. For specific treatments and risk assessment, Endo 10 ( 32 ) focuses on diagnosis in endodontics, whereas the OSA-Risk Assessment Tool ( 27 ) assesses the risk of obstructive sleep apnea. The useful tools for teledentistry and remote diagnosis was Remote-I, which facilitates remote screening for oral diseases, ideal for contexts where patients cannot physically visit clinics. ( 39 ) Telediagnostic services have demonstrated high accuracy rates, comparable to face-to-face examinations, making them a reliable alternative for clinical support, especially in remote areas. ( 64 ) For the evaluation of prosthodontics and prosthesis adjustments, Geomagic ( 31 ) is available, a tool that ensures that prostheses such as dentures fit correctly to the models of the patient's mouth. For research data development and management NutriOdonto ( 16 ) it facilitates the management of epidemiological data in oral health, which is crucial for research and diagnosis using the epidemiological method. In addition, 3D Slicer ( 34 ) and WearCompare ( 22 , 23 ) used 3D models to evaluate tooth movements and adjustments during treatment. The specialized tools for diagnosis and prevention found in the present study were Apox ( 17 ) , which analyzes panoramic X-rays providing essential clinical details such as the presence of implants and crowns, the Artificial IntelligenceToolbox ( 28 ) , which detects periodontal bone loss using AI models, and Boneprox ( 26 ) , which assesses the risk of osteoporosis through dental X-rays. Kolokythas et al. ( 65 ) argued that AI in dentistry simplifies complex protocols, helps deliver high-quality care, and improves decision-making skills, thus benefiting both doctors and patients. By using large data sets and learning patterns, machine learning can predict disease risks and aid early intervention, which could have a significant impact on patients' lives. ( 66 ) In addition, artificial intelligence applications can help doctors diagnose oral diseases, optimize treatment plans, and improve treatment outcomes, especially in pediatric dentistry. ( 67 ) The large number of computer tools found in this study focused on the diagnosis and management of specific diseases. DoctOral ( 38 ) focused on the diagnosis of oral lesions. LinguAPP ( 18 ) it helps in the diagnosis of soft tissue injuries in the oral cavity. MSC ( 33 ) can be used to detect pathological pulp exposure on periapical radiographs. MeMoSA ( 41 ) it facilitates the documentation of oral injuries and communication between dentists and specialists. The iHome DentalHealth-IoT ( 45 ) detects a wide range of dental problems, from cavities to periodontal diseases. ImageJ (Plugin) ( 51 ) It reduces bias in the measurement of bone loss in radiographic images. PDApp ( 52 ) predicts the potential for eruption or retention of third molars using a user-friendly interface for radiological image manipulation. Faster R-CNN ( 15 ) Detects gingivitis in orthodontic patients using intraoral imaging. The DSDapp ( 38 ) facilitates the planning of multidisciplinary aesthetic treatments. TSegNet ( 36 ) Uses Deep Learning for Dental Segmentation in 3D models. The limitations of the evidence included in the review may be derived from the insufficient information needed regarding each of the tools, such as the validation of some tools. In addition, excessive concentration in certain specialties, such as restorative dentistry, shows a possible limitation in the diversity of areas covered in the research. CONCLUSIONS The incorporation of informatics tools into dental practice has notable benefits in terms of diagnostic accuracy and efficiency in treatment planning. However, there is evidence for the need for a more robust regulatory framework to ensure the proper validation of these technologies before their clinical implementation. In addition, it is crucial to encourage the continuous training of dental professionals in the use of these technologies to maximize their benefits and ensure optimal patient care. This review underlines the importance of further research to explore the full potential of informatics applications in the dental field, particularly with regard to their integration into existing health systems and accessibility in resource-limited areas. Declarations Conflict of interest : The authors declare no conflict of interest. References Suazo Galdames IC (2024) Applications of Artificial Intelligence in Dentomaxillofacial Diagnosis. Rev Cubana Estomatol. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5976843","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":412322163,"identity":"d00217d2-7f78-447b-827c-6d40dcbcc49b","order_by":0,"name":"Alain Manuel Chaple Gil","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIiWNgGAWjYDACdgaGA0AqgYGBuYHhA7IMHy4tzHAtjA2MM4AsHpgMGx4tDDAtzDzEaOFvZn54uIDBLo9/dmPjY9s2u8T97L0HH92oYZDHpUXiMJvB4RkMycUSdw42G+e2JSf28JxLNs45xmDYhsthhxkMDvMwHEhsuJHYJp3bxpzYI5FjJp3DxpCAyxb5w+wfwFrm30hs/23ZVg/V8g+3FqAVEFs2AG1hZmw7DNGS24Zbi+FhngKgruTEjUC/SPacO27cc+aMsXFunwROv8gdb9/8mafCLnHe7eaDH36UVcu2t/cYPs75ZiPPj8v7EOcBsQSqkARWhaSrGQWjYBSMgpEJAM24V1BiJbJpAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-8571-4429","institution":"Universidad Autónoma de Chile","correspondingAuthor":true,"prefix":"","firstName":"Alain","middleName":"Manuel Chaple","lastName":"Gil","suffix":""}],"badges":[],"createdAt":"2025-02-07 01:23:32","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5976843/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5976843/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75897514,"identity":"3bb314d9-fc9d-4c0c-a48d-b6e06242a90a","added_by":"auto","created_at":"2025-02-10 10:39:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":46961,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA flow diagram for systematic reviews. (\u003cstrong\u003eSource\u003c/strong\u003e: Developed by authors)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5976843/v1/9f80271c52d248b30016169e.png"},{"id":75897526,"identity":"2b3d763b-f94e-43b0-9a07-4cd1b7f28261","added_by":"auto","created_at":"2025-02-10 10:39:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1088688,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5976843/v1/9908233c-ef86-48c7-94f4-3e12fb7f6be9.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eInformatic tools for diagnosis in dentistry. A compilation review\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eDentistry, like other medical disciplines, has undergone a significant digital transformation over the past few decades. The incorporation of digital technologies has improved the efficiency, precision, and scope of dental procedures, positively affecting both professionals and patients. However, the availability of a wide range of digital tools for diagnosis in dentistry poses a significant challenge due to the mercantilist stocks of the most widely used.\u003csup\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eA health informatics tool is a system or platform designed to acquire, process, interpret, and use healthcare data efficiently.\u003csup\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/sup\u003e These tools aim to improve patient safety by identifying the hazards and risks associated with health information technology.\u003csup\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/sup\u003e They play a crucial role in improving health service delivery by ensuring that the information reaches the right person at the right time. Such tools help in decision making, access to information, disease prevention, and improved communication, allowing patients and their families to manage their health effectively. These tools span various technologies, such as wearable devices, passive monitoring tools, and smart homes, providing personalized interventions based on patient-generated health data.\u003csup\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eComputer tools are increasingly used in dental practice for diagnostic purposes. These tools use intelligent systems and machine learning algorithms to analyze dental images and detect various dental conditions such as periodontal disease, alveolar bone loss, and dental pathologies. The use of deep convolutional neural networks (CNN) has yielded promising results in accurately detecting and classifying these conditions based on radiographic images.\u003csup\u003e(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/sup\u003e In addition, machine vision algorithms applied to high-resolution images can help diagnose periapical lesions and plan endodontic treatment.\u003csup\u003e(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/sup\u003e Computerized dental office systems also contribute to improved efficiency and diagnostic tasks, such as early detection of cavities, through image processing software and digital X-rays.\u003csup\u003e(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/sup\u003e In addition, computer systems that implement neural networks can process dental images to detect and diagnose pathological conditions, providing a graphical user interface for visualization and correlation with dental medical records.\u003csup\u003e(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/sup\u003e These informatics tools offer valuable support for dental diagnosis, treatment planning, and patient education, and improve the overall quality of dental care.\u003csup\u003e(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe proliferation of computer tools for diagnosis in dentistry presents opportunities, but also generates a series of questions and difficulties for dentists.\u003csup\u003e(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/sup\u003e There is a wide variety of these, from radiographic image analysis software to treatment design and planning systems, to augmented reality applications and intraoral scanners. This diversity makes it difficult to identify the most appropriate tools for each case.\u003csup\u003e(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe identification and selection of appropriate informatics tools for diagnosis in dentistry is crucial to improve diagnostic accuracy, as they can provide additional and more accurate information for diagnosis, allowing for better treatment planning.\u003csup\u003e(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/sup\u003e They optimize workflow by being able to streamline diagnostic processes, improving efficiency and productivity and can help visualize, explain problems and treatment options to the patient, improving understanding and decision-making.\u003csup\u003e(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003ePurpose Definition\u003c/h3\u003e\n\u003cp\u003eThe purpose of this review was to identify the informatics tools reported in the scientific literature that have been tested to aid diagnosis in dental practice. Based on this approach, the research problem is shown through the following question: What are the computer tools reported by science that could help establish diagnoses in dental practice?\u003c/p\u003e "},{"header":"OBJECTIVE","content":"\u003cp\u003eThe objective of this study was to compile the computer tools available in the scientific literature aimed at helping the diagnosis in dentistry.\u003c/p\u003e"},{"header":"METHODOLOGY","content":"\u003cp\u003eA scoping review was conducted between January and April 2024. The \u003cem\u003ePubMed\u003c/em\u003e, \u003cem\u003eScopus\u003c/em\u003e, and \u003cem\u003eWeb of Science\u003c/em\u003e (WOS) databases were used for this development. The reporting guidelines for literature reviews described in the PRISMA guidelines were followed.\u003c/p\u003e\n\u003ch3\u003eInclusion criteria\u003c/h3\u003e\n\u003cp\u003eThis manuscript includes original research-type articles, articles that report the usefulness of a computer/technological tool that helps diagnosis in dental practice, and articles published in the last 20 years (period 2004\u0026ndash;2024) and written in English and Spanish.\u003c/p\u003e\n\u003ch3\u003eExclusion Criteria\u003c/h3\u003e\n\u003cp\u003eReview articles of any kind (narrative, systematic, and meta-analyses), letters, case presentations, books, theses, \u003cem\u003epreprints\u003c/em\u003e, articles describing computer tools for diagnosis unrelated to stomatology, articles describing generic software for the visualization of images taken with CAD-CAM or similar equipment whose validations are frankly demonstrated, articles written in languages other than English and Spanish and Articles that did not specify a name for the tool they report were excluded.\u003c/p\u003e\n\u003ch3\u003eSearch strategy\u003c/h3\u003e\n\u003cp\u003eA search was carried out using advanced formulations on the three platforms described in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Entered keywords combined with wildcards and Boolean operators were displayed.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFormulations of the search by database used\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDatabase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormulation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePubmed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e((application[Title/Abstract]) OR (program[Title/Abstract]) OR (tool[Title/Abstract]) OR (informatic[Title/Abstract]) OR (computer[Title/Abstract]) OR (digital[Title/Abstract]) OR (apk[Title/Abstract]) OR (system[Title/Abstract]) OR (automatic[Title/Abstract])) AND ((dental[Title/Abstract]) OR (dentistry[Title/Abstract])) AND ((diagnosis[Title/Abstract]) OR (diagnoses[Title/Abstract]))\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScopus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(TITLE-ABS-KEY (program*) OR TITLE-ABS-KEY (application*) OR TITLE-ABS-KEY (tool*) OR TITLE-ABS-KEY (comput*) TITLE-ABS-KEY (informatic*) OR TITLE-ABS-KEY (apk) OR TITLE-ABS-KEY (system*) OR TITLE-ABS-KEY (digital*) OR TITLE-ABS-KEY (automa*)) AND (TITLE-ABS-KEY (dent*)) AND (TITLE-ABS-KEY (diagnos*))\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(TI=(program*) OR TI=(application*) OR TI=(tool*) OR TI=(comput*) OR TI=(informatic*) OR TI=(apk) OR TI=(system*) OR TI=(digital*) OR TI=(automa*)) AND (TI=(dent*)) AND (TI=(diagn*))\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMethodology for the selection of articles\u003c/h2\u003e \u003cp\u003eIn the databases where the searches were performed, automated filters were applied to discard articles that were not of the type described in the inclusion criteria and to establish the publication time range.\u003c/p\u003e \u003cp\u003eRecords extracted from the databases were exported to \u003cem\u003eClartivates\u003c/em\u003e Analytics EndNote 21. In this same system, duplicate articles that did not meet the eligibility criteria were decanted at first glance.\u003c/p\u003e \u003cp\u003eCalibration was performed among the authors to evaluate the articles to be selected. The degree of coincidence of the evaluations made by the reviewers was determined using Orwin's method of 1994, and a Kappa statistic was performed to measure the agreement among the reviewers who would make simple decisions about inclusion/exclusion. Kappa values between 0.40 and 0.59 were considered to reflect acceptable agreement, 0.60 to 0.74 to be an adequate agreement, and 0.75 or more to reflect excellent agreement.\u003c/p\u003e \u003cp\u003eThe online tool \u003cem\u003eRayyan\u0026reg;\u003c/em\u003e was used to establish homogeneity in the authors\u0026rsquo; review on a single online platform where they had access and could centralize the results. When the two reviewers did not agree to the decision to include or exclude a registry, a third party intervened to make the final decision.\u003c/p\u003e \u003cp\u003eFirst, duplicate reports written in languages other than English and Spanish were discarded, applying obvious inclusion/exclusion criteria such as type of document and year of publication. Subsequently, articles that did not meet the criteria were discarded based on their titles and abstracts. Reports with titles and abstracts that were doubtful of being discarded or were not discarded were read to determine this. Once those articles that did not meet the criteria were discarded, the included articles were read in detail to extract the necessary data related to the research variables.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVariables\u003c/h3\u003e\n\u003cp\u003eThe following variables were extracted from each article included in the study: name and surname of the first author, year of publication, Article Title, Journal in which it was published, citation details (including volume, number, and pages), the URL or DOI of the document (preferably the DOI), name of the tool described, country where it was developed, Tool Platform (this refers to the programming language in which the tool was developed), environment (Windows, Linux, Unix, Android, iOS, or other), dental specialty to which it pays, a brief description of the usefulness of the tool, tool validation (dichotomous yes or no), open source (dichotomous yes or no), and usage charge (dichotomous yes or no). For the Payment model, \u003cem\u003efull payment\u003c/em\u003e is considered: users pay a one-time price to access the app and all its functionalities, a recurring fee (monthly, yearly, etc.) to access the application and its functionalities, or a license is purchased to use the application for a certain period or in perpetuity. \u003cem\u003eFreemium\u003c/em\u003e: The app can be used for free, but it offers optional in-app purchases to unlock additional features, exclusive content, or to remove advertising. In addition, it was included that they were free versions with basic functionalities and paid versions with more advanced functionalities. \u003cem\u003eTrial model\u003c/em\u003e: This offers a free trial period during which all features can be accessed. After the trial period, the user must pay to continue using the app and \u003cem\u003eFree Model\u003c/em\u003e: Completely free, without any payment or restrictions.\u003c/p\u003e \u003cp\u003eIn addition, the URL through which the user manual and/or instructions for the installation and use of each tool can be downloaded was included, if available.\u003c/p\u003e\n\u003ch3\u003eData management\u003c/h3\u003e\n\u003cp\u003eThe records of the variable data extracted from the included articles were captured in a Microsoft\u0026reg; Excel\u0026reg; spreadsheet to later favor the analysis and preparation of tables and figures for better understanding.\u003c/p\u003e \u003cp\u003eIn cases the articles were not sufficiently explicit, the authors were contacted via email for more information. Respondents increased the scope of the information for this research. Articles by authors who did not respond within a reasonable period of time and the information in the articles that did not appear sufficiently for data collection were excluded.\u003c/p\u003e \u003cp\u003eThe database of the articles included in the study with all the details collected according to the variables is located in the Mendeley Data repository (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17632/yx9jts8mv7.1\u003c/span\u003e\u003cspan address=\"10.17632/yx9jts8mv7.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which advocates for the reproducibility, sharing, and systematicity of data according to the principles of Open Science.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEthical aspects\u003c/h2\u003e \u003cp\u003eThe protocol for this study was approved by the Scientific Council of the \"Ana Betancourt\" Dental Clinic in Havana, Cuba.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe calibration of the authors who reviewed the articles included in the study was excellent in all cases.\u003c/p\u003e \u003cp\u003eThe search was conducted on January 28 2024. In the primary search, 3397 records were established in Pubmed, 7563 in \u003cem\u003eScopus\u003c/em\u003e, and 221 in WOS, for a total of 12648. After applying the filter of publication time (2004\u0026ndash;2024) and the type of article, there were 46 results in \u003cem\u003ePubMed\u003c/em\u003e (only for \u003cem\u003eclinical trials\u003c/em\u003e and \u003cem\u003erandomized controlled trial articles\u003c/em\u003e), 5678 in \u003cem\u003eScopus\u003c/em\u003e, and 162 in WOS, resulting in 5886 records.\u003c/p\u003e \u003cp\u003eAfter removing duplicates, 4159 articles remained, and applying the inclusion criteria of article type 3937 and excluding articles in languages other than English and Spanish, 3655 entries remained. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eWhen examining the titles and abstracts of the remaining articles, 3287 articles were excluded, leaving 368 reports that were downloaded in full text and excluded five for not exactly describing a telematic tool for diagnosis in dentistry, 95 were excluded because they lacked the necessary details for the adequate collection of information, another 11 were discarded for loss of access through URLs, and 257 were not strictly complying with the criteria inclusion/exclusion. At the end of the process, 39 reports described 36 computer tools used for diagnosis in dentistry. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the list of articles included in the study with a predominance of articles published in the International Journal of Environmental Research and Public Health\u0026rdquo;\u003csup\u003e(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/sup\u003e with 4 reports for 10.3% of the total, followed by the \"Diagnostics\u0026rdquo;\u003csup\u003e(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)\u003c/sup\u003e with three (7.7%) reports. Only O'Toole\u003csup\u003e(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/sup\u003e matched two (5.1%) articles as the first author. The years of publication of the articles ranged from 2015 to 2023, with 2022 predominating\u003csup\u003e(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/sup\u003e and 10 (25.6%) reports.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of articles included in the study with the first author's surname, year of publication, title, and journal in which they were published.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1st Author\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArticle Title\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJournal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbd El Galil\u003csup\u003e(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEvaluation of two\u0026nbsp;computer-aided design\u0026nbsp;software\u0026nbsp;on the adaptation of digitally constructed maxillary complete denture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJournal of Indian Prosthodontic Society\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbuabara\u003csup\u003e(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEvaluation of Endo 10 mobile application as diagnostic tool in endodontics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJ Clin Exp Dent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlalharith\u003csup\u003e(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA deep learning-based approach for the detection of early signs of gingivitis in orthodontic patients using faster region-based convolutional neural networks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInternational Journal of Environmental Research and Public Health\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAltukroni\u003csup\u003e(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDetection of the pathological exposure of pulp using an artificial intelligence tool: a multicentric study over periapical radiographs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBMC Oral Health\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmorim\u003csup\u003e(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSmartphone and\u0026nbsp;computer\u0026nbsp;cephalometric analysis: A trueness and precision study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRevista Portuguesa de Estomatologia, Medicina Dentaria e Cirurgia Maxilofacial\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnacleto\u003csup\u003e(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSuperimposition of 3d maxillary digital models using open-source\u0026nbsp;software\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDental Press Journal of Orthodontics\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBarreto\u003csup\u003e(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReliability of digital orthodontic setups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAngle Orthodontist\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e 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\u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConcurrent validity and reliability of cephalometric analysis using smartphone apps and\u0026nbsp;computer\u0026nbsp;software\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAngle Orthodontist\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLo Giudice\u003csup\u003e(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA new\u0026nbsp;software\u0026nbsp;architecture proposal for an evidence-based decision support system in dentistry\u003c/p\u003e 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align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMuhammed Sunnetci\u003csup\u003e(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePeriodontal bone loss detection based on hybrid deep learning and machine learning models with a user-friendly application\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBiomedical Signal Processing and Control\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNajmuddin\u003csup\u003e(\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLogicon: A third eye for caries detection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJournal of Indian Academy of Oral Medicine and Radiology\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrhan\u003csup\u003e(\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDetermining the reliability of diagnosis and treatment using artificial intelligence\u0026nbsp;software\u0026nbsp;with panoramic radiographs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eImaging Science in Dentistry\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eO'Toole\u003csup\u003e(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInvestigation into the validity of WearCompare, a purpose-built\u0026nbsp;software\u0026nbsp;to quantify erosive tooth wear progression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDental Materials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eO'Toole\u003csup\u003e(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInfluence of scanner precision and analysis\u0026nbsp;software\u0026nbsp;in quantifying three-dimensional intraoral changes: Two-factor factorial experimental design\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJournal of Medical Internet Research\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreus\u003csup\u003e(\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA new digital tool for radiographic bone level measurements in longitudinal studies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBMC Oral Health\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReyes Salgado\u003csup\u003e(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDesign of Open Code\u0026nbsp;Software\u0026nbsp;to Downs and Steiner Lateral Cephalometric Analysis with Tracing Landmarks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDigital\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRoehl\u003csup\u003e(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTooth Wear Evaluation System (TWES) 2.0\u0026mdash;Reliability of diagnosis with and without\u0026nbsp;computer-assisted evaluation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJournal of Oral Rehabilitation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSanteiro-Hermida\u003csup\u003e(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eValidation Analysis of Panoramic Dental Application (PDApp)\u0026nbsp;Software\u0026nbsp;as a Tool for Predicting Third Molar Eruption Based on Panoramic Radiograph Images\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eApplied Sciences (Switzerland)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSayar\u003csup\u003e(\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eManual tracing versus smartphone application (app) tracing: a comparative study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eActa Odontologica Scandinavica\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZadrożny\u003csup\u003e(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArtificial Intelligence Application in Assessment of Panoramic Radiographs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDiagnostics\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003en\u0026thinsp;=\u0026thinsp;39\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eSource\u003c/b\u003e: Developed by authors\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the relationship of computer tools described by the articles with the country where they were developed, year of creation, programming language, platforms on which they can be used, the specialty with which they are related, whether they were validated, open source, free, and the payment model to which they are subject. \u0026ldquo;CephNinja, \u0026rdquo;\u003csup\u003e(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e)\u003c/sup\u003e \u0026ldquo;Diagnocat, \u0026rdquo;\u003csup\u003e(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e)\u003c/sup\u003e \u0026ldquo;OneCeph\u0026rdquo;\u003csup\u003e(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e)\u003c/sup\u003e y \u0026ldquo;WearCompare\u0026rdquo;\u003csup\u003e(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/sup\u003e were the recurrent tools and that 2 (5.1%) articles each described their use. Only six \u003csup\u003e(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e)\u003c/sup\u003e (15.4%) validation processes were not referred to in the articles, and the rest were validated.\u003c/p\u003e \u003cp\u003eThe United States was the most representative country for the development of five (14.3%) of these computer tools (3D Slicer, CephNinja, Diagnocat, Geomagic, and Logicon). (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eMore informatic tools related to \"Restorative Dentistry\" have been developed than the rest of the specialties of the dental sciences with 14 (40%), followed by the tools related to \"Orthodontics\" 9 (25.7%) and those useful for \"Oral Surgery\" 5 (13.9%). (Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe most predominant programming language for tool development was Python, with 7 (19.4%), followed by C++, which was used for the development of 5 (13.8%). (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThirty (83.3%) tools were validated, and six (16.7%) were not specified due to lack of information in the articles. A total of six (16.7%) open-code tools were described, another 16 (44.4%) were not, and of the rest, no records of this data were found in the articles. In addition, 10 (27.8%) tools were declared free, 10 (27.8%) were not, and the rest were not provided in the articles. (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of informatic tools extracted from articles with general data on development and availability.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTool name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProgramming language\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePlatforms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSpecialty\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eValidated\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOpen code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eFree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePayment model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3D\u0026nbsp;Slicer\u003csup\u003e(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWindows,\u0026nbsp;macOS\u0026nbsp;y\u0026nbsp;Linux.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eFM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApox\u003csup\u003e(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNetherlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArtifical_Intelligence_Toolbox\u003csup\u003e(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTurkey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMATLAB\u0026nbsp;2020b - GUI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWindows, Android\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoneprox\u003csup\u003e(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSweden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWindows\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCephalopoint\u003csup\u003e(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMexico\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOctave\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAndroid, iOS, Linux Windows, Unix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eFM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCephNinja\u003csup\u003e(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA - India\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAndroid, iOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCranioCatch\u003csup\u003e(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTurkey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePython\u0026nbsp;(PyTorch)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAndroid, iOS, Linux Windows, Unix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eFPM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecision\u0026nbsp;Support\u0026nbsp;System\u003csup\u003e(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVisual C#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDCDNet\u003csup\u003e(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTurkey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePython\u0026nbsp;(TensorFlow)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDenti.Ai\u003csup\u003e(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFaster\u0026nbsp;R-CNN,\u0026nbsp;VGG-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAndroid, iOS, Linux Windows, Unix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eFPM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeNTNet\u003csup\u003e(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSouth Korea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiagnocat\u003csup\u003e(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePython\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAny Web Navigator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eFPM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoctOral\u003csup\u003e(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAndroid, iOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eFM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDSDapp\u003csup\u003e(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBrazil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eiOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eFPM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEndo 10\u003csup\u003e(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBrazil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAndroid, iOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eFM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaster\u0026nbsp;R-CNN\u003csup\u003e(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSaudi Arabia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePython\u0026nbsp;(TensorFlow)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLinux, Windows, macOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeomagic\u003csup\u003e(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWindows\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eiHome\u0026nbsp;DentalHealth-IoT\u003csup\u003e(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAndroid,\u0026nbsp;iOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImageJ (Plugin)\u003csup\u003e(\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNorway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWindows,\u0026nbsp;macOS,\u0026nbsp;Linux\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLinguAPP\u003csup\u003e(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJavaScript\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAndroid, iOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eFM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLogicon\u003csup\u003e(\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMSC\u003csup\u003e(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSaudi Arabia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePython\u0026nbsp;-\u0026nbsp;Yolov5-x\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWindows,\u0026nbsp;Linux,\u0026nbsp;macOS.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMask-RCNN\u003csup\u003e(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePython\u0026nbsp;(Tensorflow,\u0026nbsp;Keras)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeMoSA\u003csup\u003e(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReact\u0026nbsp;Native\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAndroid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMIMICS\u003csup\u003e(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBelgium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWindows, Linux\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eFPM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNutriOdonto\u003csup\u003e(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBrazil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAndroid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eFM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOneCeph\u003csup\u003e(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAndroid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrthoAnalyzer\u003csup\u003e(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDenmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eFPM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOSA-Risk\u0026nbsp;Assessment\u0026nbsp;Tool\u003csup\u003e(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHTML, PHP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAny Web Navigator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eFM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePDApp\u003csup\u003e(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLinux, Windows\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRDFNet \u003csup\u003e(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePython\u0026nbsp;(Pytorch)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWindows, Linux, MacOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRemote-I\u003csup\u003e(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAndroid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTG\u0026nbsp;Dental\u003csup\u003e(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTWES\u003csup\u003e(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMariaDB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWindows, macOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eFPM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTSegNet\u003csup\u003e(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHong\u0026nbsp;Kong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eiOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eFPM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWearCompare\u003csup\u003e(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnited Kingdom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWindows, Linux\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eFM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003en\u0026thinsp;=\u0026thinsp;36\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003cb\u003eSource\u003c/b\u003e: Developed by authors - \u003cb\u003eCaption\u003c/b\u003e: O (Orthodontics), OS (Oral surgery), OR (Oral Rehabilitation), RD (Restorative dentistry), P (Periodontics), E (Endodontics), NR (Not referred), FPM (Full Paid Model), FM (Free Model), F (Freemium), TM (Test Model)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the grouping of the tools according to the specialties in which they are useful for diagnosis to facilitate consultation according to the affinity of interested readers.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribuci\u0026oacute;n de herramientas inform\u0026aacute;ticas seg\u0026uacute;n especialidad en la que ayudan al diagn\u0026oacute;stico.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRESTORATIVE DENTISTRY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eORTHODONTICS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eORAL SURGERY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePERIODONTICS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePROSTHODONTICS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eENDODONTICS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApox\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3D\u0026nbsp;Slicer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiagnocat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArtifical_Intelligence_Toolbox\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGeomagic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEndo 10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoneprox\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCephalopoint\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDoctOral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeNTNet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTWES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMask-RCNN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCranioCatch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCephNinja\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLinguAPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFaster\u0026nbsp;R-CNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"11\" rowspan=\"12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"11\" rowspan=\"12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecision\u0026nbsp;Support\u0026nbsp;System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMIMICS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMeMoSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eImageJ (Plugin)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDCDNet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOneCeph\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePDApp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"9\" rowspan=\"10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDenti.Ai\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrthoAnalyzer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"8\" rowspan=\"9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDSDapp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOSA-Risk\u0026nbsp;Assessment\u0026nbsp;Tool\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eiHome\u0026nbsp;DentalHealth-IoT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTG\u0026nbsp;Dental\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLogicon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTSegNet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNutriOdonto\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDFNet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemote-I\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWearCompare\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003en\u0026thinsp;=\u0026thinsp;36\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe search engines used to carry out this research were Scopus, Web of Science, and PubMed, which are essential databases for systematic reviews owing to their wide coverage and unique characteristics. Scopus, as highlighted by Schwager and Schalk\u003csup\u003e(\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e)\u003c/sup\u003e, demonstrated high sensitivity in retrieving relevant articles, while Web of Science, as shown in Kumpulainen and Sepp\u0026auml;nen\u003csup\u003e(\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e)\u003c/sup\u003e, efficiently identified additional studies by searching for citations. PubMed, which is renowned for its extensive coverage of the biomedical literature, is crucial for health-related systematic reviews. The use of these databases ensures a thorough search process, as highlighted in the work of Alfandaria and Taylor\u003csup\u003e(\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e)\u003c/sup\u003e, who found that replicability varied from platform to platform. Each database contributes to minimizing bias and maximizing the retrieval of relevant studies, aligning with best practices for systematic literature searches.\u003csup\u003e(\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e)\u003c/sup\u003e Based on these approaches, the reason why these databases were chosen for the search of articles in this research is shown.\u003c/p\u003e \u003cp\u003eThe tools described in the results have great potential to improve diagnosis, treatment planning, and information management in the field of dentistry and can be grouped to provide a complete overview and analyze them in detail according to their specialization and functions.\u003c/p\u003e \u003cp\u003eAmong the tools for cephalometric analysis and diagnosis, Cephalopoint\u003csup\u003e(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/sup\u003e and OneCeph\u003csup\u003e(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e)\u003c/sup\u003e offer digital cephalometric analysis, facilitating the diagnosis and planning of orthodontic treatments. CephNinja\u003csup\u003e(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e)\u003c/sup\u003e allows digital cephalometric plotting on mobile devices, further streamlining the process. In addition, MIMICS\u003csup\u003e(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e)\u003c/sup\u003e provides three-dimensional cephalometric evaluation of facial soft tissues, which is crucial for assessing facial harmony and orthodontic planning.\u003c/p\u003e \u003cp\u003eStudies by da Fonseca Reis et al.\u003csup\u003e(\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e)\u003c/sup\u003e and Prince et al.\u003csup\u003e(\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e)\u003c/sup\u003e argue that computer tools that enable cephalometric tracking offer several advantages in orthodontic diagnosis and treatment planning. These tools improve the accuracy, reproducibility, and efficiency of cephalometric image analyses. On the other hand, they eliminate human errors associated with manual tracking, allow automatic detection of landmarks, and offer a high level of agreement with manual methods.\u003csup\u003e(\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e)\u003c/sup\u003e In addition, according to Reyes Salgado\u003csup\u003e(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/sup\u003e computerized cephalometric tracing methods are more reliable and consistent than manual tracing, ensuring accurate measurements for treatment proposals and diagnostic hypotheses.\u003c/p\u003e \u003cp\u003eFor the diagnosis of cavities and other dental diseases, we found Logicon,\u003csup\u003e(\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e)\u003c/sup\u003e RDFNet, \u003csup\u003e(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e)\u003c/sup\u003e and DCDNet\u003csup\u003e(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/sup\u003e, which specialize in detecting cavities in dental imaging, while Mask-RCNN\u003csup\u003e(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e)\u003c/sup\u003e focuses on the detection and classification of periapical lesions. In addition, Denti.Ai\u003csup\u003e(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/sup\u003e y Diagnocat\u003csup\u003e(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e)\u003c/sup\u003e uses AI to identify a variety of dental pathologies, with Denti.Ai\u003csup\u003e(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/sup\u003e also focusing on implants and crowns. On the other hand, TG Dental\u003csup\u003e(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)\u003c/sup\u003e classifies malocclusions without manual identification of landmarks using AI to speed up diagnosis and planning.\u003c/p\u003e \u003cp\u003eComputer tools for diagnosing tooth decay from images offer objective verification, aid in doctor-patient communication, teledentistry, and potentially improve diagnostic accuracy and efficiency in the detection of oral diseases.\u003csup\u003e(\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e)\u003c/sup\u003e Studies such as Tareq et al. \u003csup\u003e(\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e)\u003c/sup\u003e argue that these applications make it possible to predict dental cavitations from non-standardized photographs with reasonable clinical accuracy, improving access to oral health care in resource-limited areas. In addition, the use of deep learning in panoramic images makes it possible to accurately detect various tooth-related diseases in real-time, helping to plan treatment in time and reducing the risk of misdiagnosis.\u003csup\u003e(\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTo support clinical decisions, we have a Decision Support System\"\u003csup\u003e(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e)\u003c/sup\u003e that standardizes the decision-making process in dentistry and offers evidence-based therapeutic options. For specific treatments and risk assessment, Endo 10\u003csup\u003e(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e)\u003c/sup\u003e focuses on diagnosis in endodontics, whereas the OSA-Risk Assessment Tool\u003csup\u003e(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/sup\u003e assesses the risk of obstructive sleep apnea.\u003c/p\u003e \u003cp\u003eThe useful tools for teledentistry and remote diagnosis was Remote-I, which facilitates remote screening for oral diseases, ideal for contexts where patients cannot physically visit clinics.\u003csup\u003e(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e)\u003c/sup\u003e Telediagnostic services have demonstrated high accuracy rates, comparable to face-to-face examinations, making them a reliable alternative for clinical support, especially in remote areas.\u003csup\u003e(\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eFor the evaluation of prosthodontics and prosthesis adjustments, Geomagic\u003csup\u003e(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e)\u003c/sup\u003e is available, a tool that ensures that prostheses such as dentures fit correctly to the models of the patient's mouth. For research data development and management NutriOdonto\u003csup\u003e(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/sup\u003e it facilitates the management of epidemiological data in oral health, which is crucial for research and diagnosis using the epidemiological method.\u003c/p\u003e \u003cp\u003eIn addition, 3D Slicer\u003csup\u003e(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/sup\u003e and WearCompare\u003csup\u003e(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/sup\u003e used 3D models to evaluate tooth movements and adjustments during treatment.\u003c/p\u003e \u003cp\u003eThe specialized tools for diagnosis and prevention found in the present study were Apox\u003csup\u003e(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/sup\u003e, which analyzes panoramic X-rays providing essential clinical details such as the presence of implants and crowns, the Artificial IntelligenceToolbox\u003csup\u003e(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/sup\u003e, which detects periodontal bone loss using AI models, and Boneprox\u003csup\u003e(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e)\u003c/sup\u003e, which assesses the risk of osteoporosis through dental X-rays.\u003c/p\u003e \u003cp\u003eKolokythas et al.\u003csup\u003e(\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e)\u003c/sup\u003e argued that AI in dentistry simplifies complex protocols, helps deliver high-quality care, and improves decision-making skills, thus benefiting both doctors and patients. By using large data sets and learning patterns, machine learning can predict disease risks and aid early intervention, which could have a significant impact on patients' lives.\u003csup\u003e(\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e)\u003c/sup\u003e In addition, artificial intelligence applications can help doctors diagnose oral diseases, optimize treatment plans, and improve treatment outcomes, especially in pediatric dentistry.\u003csup\u003e(\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe large number of computer tools found in this study focused on the diagnosis and management of specific diseases. DoctOral\u003csup\u003e(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e)\u003c/sup\u003e focused on the diagnosis of oral lesions. LinguAPP\u003csup\u003e(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/sup\u003e it helps in the diagnosis of soft tissue injuries in the oral cavity. MSC\u003csup\u003e(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e)\u003c/sup\u003e can be used to detect pathological pulp exposure on periapical radiographs. MeMoSA\u003csup\u003e(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e)\u003c/sup\u003e it facilitates the documentation of oral injuries and communication between dentists and specialists. The iHome DentalHealth-IoT\u003csup\u003e(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e)\u003c/sup\u003e detects a wide range of dental problems, from cavities to periodontal diseases. ImageJ (Plugin) \u003csup\u003e(\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e)\u003c/sup\u003e It reduces bias in the measurement of bone loss in radiographic images. PDApp\u003csup\u003e(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e)\u003c/sup\u003e predicts the potential for eruption or retention of third molars using a user-friendly interface for radiological image manipulation. Faster R-CNN\u003csup\u003e(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/sup\u003e Detects gingivitis in orthodontic patients using intraoral imaging. The DSDapp\u003csup\u003e(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e)\u003c/sup\u003e facilitates the planning of multidisciplinary aesthetic treatments. TSegNet\u003csup\u003e(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e)\u003c/sup\u003e Uses Deep Learning for Dental Segmentation in 3D models.\u003c/p\u003e \u003cp\u003eThe limitations of the evidence included in the review may be derived from the insufficient information needed regarding each of the tools, such as the validation of some tools. In addition, excessive concentration in certain specialties, such as restorative dentistry, shows a possible limitation in the diversity of areas covered in the research.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThe incorporation of informatics tools into dental practice has notable benefits in terms of diagnostic accuracy and efficiency in treatment planning. However, there is evidence for the need for a more robust regulatory framework to ensure the proper validation of these technologies before their clinical implementation. In addition, it is crucial to encourage the continuous training of dental professionals in the use of these technologies to maximize their benefits and ensure optimal patient care. This review underlines the importance of further research to explore the full potential of informatics applications in the dental field, particularly with regard to their integration into existing health systems and accessibility in resource-limited areas.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003e \u003cb\u003eConflict of interest\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSuazo Galdames IC (2024) Applications of Artificial Intelligence in Dentomaxillofacial Diagnosis. Rev Cubana Estomatol. 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Med Studies/Studia Medyczne 38(1):68\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5114/ms.2022.115149\u003c/span\u003e\u003cspan address=\"10.5114/ms.2022.115149\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Universidad Autónoma de Chile","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"(MeSH): Clinical Informatics, Medical Informatics Applications, Dental Informatics, Medical Informatics Computing, Oral Diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-5976843/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5976843/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe objective of this study was to compile the computer tools available in the scientific literature aimed at helping the diagnosis in dentistry. A scoping review was conducted using \u003cem\u003ePubMed\u003c/em\u003e, \u003cem\u003eScopus\u003c/em\u003e, and \u003cem\u003eWeb of Science\u003c/em\u003e. Were include articles that reported the usefulness of a computer/technological tool that helps diagnosis in dental practice, published in the last 20 years in English and Spanish. Online tool \u003cem\u003eRayyan\u0026reg;\u003c/em\u003e was used to establish homogeneity in the review of the authors and centralize the results. In total, 12648 records were retrieved from the databases. After decantation, 39 reports described 36 computer tools used to help for diagnosis in dentistry. More informatic tools related to \"Restorative Dentistry\u0026rsquo; have been developed than the rest of the specialties 14 (40%). Python was the predominant programming language, 83.3% of the tools were validated, and 27.8% were free. Informatics tools in dentistry enhance the diagnosis and treatment planning. However, a robust regulatory framework is required for validation prior to clinical implementation. Continuous training of dental professionals using these technologies is crucial to maximize their benefits and ensure optimal patient care. More research is needed to explore the potential of informatics applications in dentistry, their integration into existing health systems, and their accessibility in resource-limited areas.\u003c/p\u003e","manuscriptTitle":"Informatic tools for diagnosis in dentistry. A compilation review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-10 10:39:11","doi":"10.21203/rs.3.rs-5976843/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"32be5926-8518-4b67-a9ed-ac8e79bd2295","owner":[],"postedDate":"February 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":43969444,"name":"Dentistry"},{"id":43969445,"name":"Medical Informatics"}],"tags":[],"updatedAt":"2025-02-10T10:39:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-10 10:39:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5976843","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5976843","identity":"rs-5976843","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}