The Integration of Artificial Intelligence in Orthodontic Diagnosis and Treatment Planning: A PRISMA-Guided Systematic Review

The Integration of Artificial Intelligence in Orthodontic Diagnosis and Treatment Planning: A PRISMA-Guided Systematic 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 The Integration of Artificial Intelligence in Orthodontic Diagnosis and Treatment Planning: A PRISMA-Guided Systematic Review Salah M. Ben Hafedh, Ahmad Hashridz Bin Ruslan, Ramy Ishaq, Rozita Hassan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8108220/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 Background Artificial intelligence (AI) applications in orthodontics are rapidly expanding across diagnosis, image analysis, and treatment planning. Methods A PRISMA-guided systematic review was conducted. PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar were searched from 2010 to 16 September 2025. Original studies in orthodontics that used AI or machine learning for diagnosis, prediction, image analysis, or treatment planning were eligible. Two reviewers independently screened records, extracted data, and assessed risk of bias using QUADAS-2 for diagnostic accuracy studies and PROBAST for prediction model studies. Owing to heterogeneity in study design, datasets, and outcome metrics, results were synthesized narratively. Results Of 1,162 records identified, 1,008 remained after duplicate removal and were screened by title and abstract. A total of 154 full-text articles were assessed for eligibility, and 45 met the inclusion criteria. Frequent AI tasks included cephalometric landmark detection, malocclusion classification, extraction-decision support, treatment duration prediction, and cone-beam computed tomography (CBCT)-based segmentation. Many studies reported high accuracies for cephalometric landmark detection (mean radial error 80%) and malocclusion classification (accuracies > 85%). However, risk-of-bias concerns, particularly in analysis and validation domains, were common, and external validation was infrequent. Conclusions AI models show promising performance for orthodontic diagnosis and treatment planning and may enhance efficiency and standardization of care. Nevertheless, non-standardized outcome measures, limited external validation, and insufficient reporting of model development and evaluation currently restrict clinical translation. Larger, multicenter datasets, standardized benchmarks, and robust validation—ideally following AI-specific reporting guidelines—are required before routine clinical adoption. Registration PROSPERO CRD420251134644. Figures Figure 1 1. Introduction In recent years, artificial intelligence (AI) has moved from being a futuristic concept to an everyday reality in healthcare. Orthodontics, where precision in diagnosis and treatment planning is essential for long-term functional and aesthetic outcomes, has been particularly affected by this transformation [ 1 – 3 ]. AI tools can help reduce diagnostic errors, streamline clinical workflows, and allow orthodontists to dedicate more time to direct patient care [ 4 – 6 ]. Machine learning (ML) and deep learning (DL), as subfields of AI, are particularly powerful for analyzing diverse clinical datasets, including cephalometric radiographs, CBCT scans, panoramic images, intraoral photographs, and structured patient records [ 7 – 11 ]. Convolutional neural networks (CNNs), among the most widely applied DL architectures, have shown high accuracy in cephalometric landmark detection, malocclusion classification, and image segmentation, often matching or surpassing the performance of human examiners [ 12 – 16 ]. Landmark detection studies frequently report mean radial errors below 2 mm and successful detection rates above 80%, while classification accuracies for malocclusion often exceed 85% [ 17 – 20 ]. Beyond diagnosis, AI is increasingly applied to complex clinical decision-making, such as predicting treatment duration, identifying when extractions are necessary, and estimating the need for orthognathic surgery [ 21 – 27 ]. These applications indicate that AI is evolving from a purely experimental tool to a clinically relevant adjunct in routine orthodontic practice [ 28 – 30 ]. However, important challenges remain. Many models are trained on single-center or demographically narrow datasets, which may limit generalizability. Ethical and regulatory issues—including data privacy, algorithmic bias, and medico-legal responsibility—also require careful consideration. Moreover, the “black-box” nature of many DL models can impede clinician trust in AI-generated recommendations [ 31 – 35 ]. This PRISMA-guided systematic review synthesizes evidence from original studies published between 2010 and 2025 that evaluated AI for orthodontic diagnosis and treatment planning. The objectives are to (1) summarize the types of AI tasks and methodologies used in orthodontics, (2) evaluate the reported diagnostic and predictive performance of these systems, and (3) identify current limitations and future research priorities for safe and effective AI integration into orthodontic care [ 36 – 40 ]. 2. Materials and Methods 2.1 Protocol and registration This systematic review was conducted in accordance with the PRISMA 2020 statement and relevant AI-focused reporting extensions when applicable. The review protocol was prospectively registered with PROSPERO (registration number CRD420251134644). 2.2 Information sources Electronic searches were carried out in PubMed/MEDLINE, Scopus, Web of Science (Core Collection), and Google Scholar. Databases were searched for articles published between 1 January 2010 and 16 September 2025. Search strategies combined keywords and MeSH terms related to “artificial intelligence,” “machine learning,” “deep learning,” “orthodontics,” “diagnosis,” “treatment planning,” and “cephalometrics.” Detailed, database-specific search strings are provided in the Supplementary material (Search strategies document). 2.3 Eligibility criteria Inclusion criteria Peer-reviewed original research articles. Studies involving AI (including ML, DL, CNNs, or hybrid techniques) applied to orthodontic diagnosis, prediction, image analysis, or treatment planning. Human clinical data or imaging (e.g., 2D cephalometric radiographs, CBCT, panoramic radiographs, intraoral photographs, or structured clinical records). Reporting of at least one quantitative performance metric (e.g., accuracy, sensitivity/specificity, AUC, mean radial error, Dice similarity coefficient, mean absolute error). Articles published in English between January 2010 and June 2025. Exclusion criteria Narrative reviews, systematic reviews, scoping reviews, editorials, commentaries, conference abstracts, and letters without original data. Studies not primarily focused on orthodontics (e.g., general dentistry, restorative dentistry, endodontics) or AI applications not related to diagnosis or treatment planning. Studies that did not clearly describe their AI methodology or lacked a human reference standard. Animal or in-vitro studies. 2.4 Data extraction and quality assessment Two reviewers independently screened titles and abstracts, followed by full-text assessment of potentially eligible studies. Disagreements were resolved through discussion or consultation with a third reviewer. Data were extracted using a standardized form and included: first author, publication year, country, imaging modality, primary AI task (e.g., landmark detection, classification, prediction, segmentation), dataset size, AI architecture or algorithm, reference standard (e.g., expert orthodontist annotations), primary performance metrics, and whether internal and/or external validation was performed. Risk of bias in diagnostic accuracy studies was assessed using the QUADAS-2 tool, which evaluates four domains: patient selection, index test, reference standard, and flow/timing [ 2 ]. Prediction model studies were assessed using the PROBAST tool, which covers participants, predictors, outcomes, and analysis domains [ 3 ]. Summary risk-of-bias assessments are presented in the Supplementary QUADAS-2 and PROBAST tables, with study-level details provided separately. 2.5 Synthesis methods The included studies displayed substantial heterogeneity in study design (diagnostic accuracy vs prediction models), imaging modalities (2D radiographs, CBCT, panoramic radiographs, intraoral photographs, clinical records), AI architectures, and outcome measures (e.g., landmark error, Dice similarity coefficient, AUC, accuracy, MAE). Because of this variability, quantitative meta-analysis was not feasible. Instead, a structured narrative synthesis was conducted in accordance with PRISMA 2020 guidance. Studies were grouped by primary AI task (e.g., cephalometric landmark detection, malocclusion classification, extraction decision support, treatment duration prediction, CBCT segmentation), and key performance indicators were summarized within each category. 3. Results The PRISMA 2020 flow diagram summarizing study selection is shown in Fig. 1 near here (PRISMA 2020 flow diagram of study identification, screening, eligibility, and inclusion). Figure 1. PRISMA 2020 flow diagram of study identification, screening, eligibility, and inclusion. 3.1 Study selection The database search identified 1,162 records. After removal of duplicates, 1,008 records remained for title and abstract screening. Of these, 854 records were excluded. A total of 154 full-text articles were assessed for eligibility. One hundred and nine full-text articles were excluded for the following main reasons: not orthodontics-related AI studies (n = 35), review/editorial/commentary articles (n = 28), inappropriate or irrelevant study design (n = 20), insufficient diagnostic or treatment-planning detail (n = 16), and duplicate publication (n = 10). Finally, 45 studies met all inclusion criteria and were included in the qualitative synthesis. 3.2 Study characteristics Table 1 summarizes the characteristics of the 45 included studies. The majority were retrospective observational studies conducted in academic or specialist orthodontic settings. Common imaging modalities included 2D cephalometric radiographs, CBCT, panoramic radiographs, and intraoral photographs. The main AI tasks and applications were: Cephalometric landmark detection and cephalometric analysis; Malocclusion and skeletal pattern classification; Extraction-decision support and prediction of orthognathic surgery necessity; Prediction of treatment duration and treatment outcomes; CBCT-based 3D segmentation of dental and skeletal structures. Sample sizes varied widely, ranging from fewer than 100 images or patients to more than 1,000. CNN-based DL approaches were predominant for image-based tasks, whereas traditional ML algorithms (e.g., random forests, support vector machines, gradient boosting) were more commonly used for structured clinical data and prediction tasks. Table 1. Study Characteristics of Included Studies (n = 45) Discussion This PRISMA-guided systematic review demonstrates that AI is no longer a distant prospect but an emerging reality in orthodontic care. Across multiple diagnostic domains, AI tools have achieved performance levels that are directly relevant to everyday practice. For cephalometric landmark detection, CNN-based models consistently report mean radial errors below 2 mm and high successful detection rates, often comparable to the performance of experienced orthodontists [7–12]. Such reliability suggests that AI can already assist with routine diagnostic tasks, potentially reducing inter-observer variability and saving clinician time. Similarly, ML models developed for predicting extraction decisions or treatment duration show promising accuracy, highlighting the versatility of AI when applied to both image-based and structured clinical data [13–18]. Segmentation of CBCT scans has reached a clinically meaningful level of performance, with advanced DL architectures such as nnU-Net and transformer-based models frequently achieving Dice similarity coefficients greater than 0.90 for teeth and alveolar bone [19–22]. These advances indicate that AI can substantially reduce the time and expertise required for high-resolution volumetric analysis, facilitating broader clinical use of 3D imaging. Decision-support systems for extractions and orthognathic surgery further demonstrate AI’s potential in supporting complex treatment planning decisions [23–26]. Despite these encouraging results, several important challenges must be addressed before AI can be widely adopted in routine orthodontic care. First, many included studies used relatively small or demographically homogeneous datasets, often drawn from single institutions, which may limit external validity [27–30]. Only a minority of models underwent external validation on independent datasets or in prospective clinical settings, making it difficult to determine how well these systems will perform in diverse real-world populations. Second, transparency and interpretability remain major concerns. Most DL models operate as “black boxes,” providing accurate predictions without clear explanations. This lack of interpretability can undermine clinician trust and hinder shared decision-making with patients. Explainable AI (XAI) approaches—including saliency maps, attention mechanisms, and feature-importance analyses—are therefore essential to clarify model reasoning and to support responsible clinical use [31–33]. Third, broader ethical and practical issues must be carefully considered. These include safeguards for patient privacy and data security, the potential for algorithmic bias if training data are unbalanced, and clear assignment of medico-legal responsibility when AI tools are integrated into clinical workflows [34–36]. Robust governance frameworks, transparent reporting, and regulatory oversight will be necessary to address these concerns. Future research should prioritize: Large, multicenter, and demographically diverse datasets to improve generalizability and reduce algorithmic bias. Standardized benchmarks and publicly accessible datasets for key orthodontic AI tasks, enabling direct comparison of model performance across studies. Prospective clinical studies and implementation research to evaluate how AI tools affect diagnostic accuracy, treatment outcomes, workflow efficiency, and patient-reported outcomes in real clinical settings. Adherence to AI-specific reporting guidelines (e.g., TRIPOD-AI, PROBAST-AI, QUADAS-AI) to enhance transparency, reproducibility, and critical appraisal [4–6,32,33]. Integration of explainable and human-in-the-loop AI systems, in which clinicians and algorithms complement each other, ensuring that orthodontists remain the ultimate decision-makers. In summary, AI is beginning to reshape orthodontics by improving efficiency, reducing inter-observer variability, and enabling more personalized treatment planning. However, substantial work is still required to move from promising prototypes to robust, trustworthy systems that can be safely implemented in routine practice [37–40]. Conclusion AI currently demonstrates strong performance across multiple orthodontic diagnostic and treatment-planning tasks. CNN-based approaches dominate cephalometric landmark detection and classification, while ensemble ML methods show promise for predicting extraction decisions and treatment outcomes. Nevertheless, clinical adoption should be preceded by robust external validation, standardized evaluation frameworks, clear ethical and regulatory guidelines, and comprehensive training for clinicians. With these safeguards in place, AI can evolve from an auxiliary tool into an integral component of high-quality, patient-centred orthodontic care. Declarations Ethics approval and consent to participate Not applicable (no primary data were collected). Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analysed during the current study (extraction sheet, PRISMA checklist, QUADAS-2 and PROBAST assessments) are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding No specific funding was received for this work. Authors’ contributions Conceptualization: SMBH, AHBR, RI. Methodology and search strategy: SMBH, AHBR, RH. Screening and data extraction: SMBH, AAM. Risk-of-bias assessment (QUADAS-2 and PROBAST): SMBH, RI, RH. Writing – original draft: SMBH. Writing – review and editing, and supervision: AHBR, RI, RH. All authors read and approved the final manuscript. References Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155(8):529–36. Moons KG, Wolff RF, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies. Ann Intern Med. 2019;170(1):51–8. Collins GS, Dhiman P, Navarro CLA, Ma J, Hooft L, Smidt N, et al. Protocols for reporting AI-based diagnostic accuracy studies (TRIPOD-AI, PROBAST-AI). 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Supplementary Files SupplementaryOverviewS1S4.docx S1PRISMA2020Checklist.docx S2QUADAS2RiskOfBias.docx S3PROBASTRiskOfBias.docx S4SearchStrategies.docx Table1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":76793,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA 2020 flow diagram of study identification, screening, eligibility, and inclusion.\u003c/p\u003e","description":"","filename":"PRISMAFlowDiagram.png","url":"https://assets-eu.researchsquare.com/files/rs-8108220/v1/7ebc01d68f5e28755f9c15ac.png"},{"id":99150465,"identity":"8b5b174c-12bd-466c-954d-a63efd3b981d","added_by":"auto","created_at":"2025-12-29 10:10:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":508701,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8108220/v1/16cec205-e512-42d0-a374-4aa22948c08c.pdf"},{"id":98323556,"identity":"6b0cc622-2603-4c8c-ba43-5517256d052c","added_by":"auto","created_at":"2025-12-16 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16:56:27","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":37840,"visible":true,"origin":"","legend":"","description":"","filename":"S4SearchStrategies.docx","url":"https://assets-eu.researchsquare.com/files/rs-8108220/v1/de3ada9b471cbd66ea47c6ff.docx"},{"id":98438003,"identity":"663f576e-89c8-4795-b2bc-022aed44f584","added_by":"auto","created_at":"2025-12-17 16:58:23","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":29074,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8108220/v1/824de02643ac201dc5ceb96e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Integration of Artificial Intelligence in Orthodontic Diagnosis and Treatment Planning: A PRISMA-Guided Systematic Review","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn recent years, artificial intelligence (AI) has moved from being a futuristic concept to an everyday reality in healthcare. Orthodontics, where precision in diagnosis and treatment planning is essential for long-term functional and aesthetic outcomes, has been particularly affected by this transformation [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. AI tools can help reduce diagnostic errors, streamline clinical workflows, and allow orthodontists to dedicate more time to direct patient care [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMachine learning (ML) and deep learning (DL), as subfields of AI, are particularly powerful for analyzing diverse clinical datasets, including cephalometric radiographs, CBCT scans, panoramic images, intraoral photographs, and structured patient records [\u003cspan additionalcitationids=\"CR8 CR9 CR10\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Convolutional neural networks (CNNs), among the most widely applied DL architectures, have shown high accuracy in cephalometric landmark detection, malocclusion classification, and image segmentation, often matching or surpassing the performance of human examiners [\u003cspan additionalcitationids=\"CR13 CR14 CR15\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Landmark detection studies frequently report mean radial errors below 2 mm and successful detection rates above 80%, while classification accuracies for malocclusion often exceed 85% [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBeyond diagnosis, AI is increasingly applied to complex clinical decision-making, such as predicting treatment duration, identifying when extractions are necessary, and estimating the need for orthognathic surgery [\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25 CR26\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. These applications indicate that AI is evolving from a purely experimental tool to a clinically relevant adjunct in routine orthodontic practice [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, important challenges remain. Many models are trained on single-center or demographically narrow datasets, which may limit generalizability. Ethical and regulatory issues\u0026mdash;including data privacy, algorithmic bias, and medico-legal responsibility\u0026mdash;also require careful consideration. Moreover, the \u0026ldquo;black-box\u0026rdquo; nature of many DL models can impede clinician trust in AI-generated recommendations [\u003cspan additionalcitationids=\"CR32 CR33 CR34\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis PRISMA-guided systematic review synthesizes evidence from original studies published between 2010 and 2025 that evaluated AI for orthodontic diagnosis and treatment planning. The objectives are to (1) summarize the types of AI tasks and methodologies used in orthodontics, (2) evaluate the reported diagnostic and predictive performance of these systems, and (3) identify current limitations and future research priorities for safe and effective AI integration into orthodontic care [\u003cspan additionalcitationids=\"CR37 CR38 CR39\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Protocol and registration\u003c/h2\u003e\u003cp\u003eThis systematic review was conducted in accordance with the PRISMA 2020 statement and relevant AI-focused reporting extensions when applicable. The review protocol was prospectively registered with PROSPERO (registration number CRD420251134644).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Information sources\u003c/h2\u003e\u003cp\u003eElectronic searches were carried out in PubMed/MEDLINE, Scopus, Web of Science (Core Collection), and Google Scholar. Databases were searched for articles published between 1 January 2010 and 16 September 2025. Search strategies combined keywords and MeSH terms related to \u0026ldquo;artificial intelligence,\u0026rdquo; \u0026ldquo;machine learning,\u0026rdquo; \u0026ldquo;deep learning,\u0026rdquo; \u0026ldquo;orthodontics,\u0026rdquo; \u0026ldquo;diagnosis,\u0026rdquo; \u0026ldquo;treatment planning,\u0026rdquo; and \u0026ldquo;cephalometrics.\u0026rdquo; Detailed, database-specific search strings are provided in the Supplementary material (Search strategies document).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Eligibility criteria\u003c/h2\u003e\u003cp\u003eInclusion criteria\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003ePeer-reviewed original research articles.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStudies involving AI (including ML, DL, CNNs, or hybrid techniques) applied to orthodontic diagnosis, prediction, image analysis, or treatment planning.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eHuman clinical data or imaging (e.g., 2D cephalometric radiographs, CBCT, panoramic radiographs, intraoral photographs, or structured clinical records).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eReporting of at least one quantitative performance metric (e.g., accuracy, sensitivity/specificity, AUC, mean radial error, Dice similarity coefficient, mean absolute error).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eArticles published in English between January 2010 and June 2025.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eExclusion criteria\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eNarrative reviews, systematic reviews, scoping reviews, editorials, commentaries, conference abstracts, and letters without original data.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStudies not primarily focused on orthodontics (e.g., general dentistry, restorative dentistry, endodontics) or AI applications not related to diagnosis or treatment planning.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStudies that did not clearly describe their AI methodology or lacked a human reference standard.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAnimal or in-vitro studies.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Data extraction and quality assessment\u003c/h2\u003e\u003cp\u003eTwo reviewers independently screened titles and abstracts, followed by full-text assessment of potentially eligible studies. Disagreements were resolved through discussion or consultation with a third reviewer.\u003c/p\u003e\u003cp\u003eData were extracted using a standardized form and included: first author, publication year, country, imaging modality, primary AI task (e.g., landmark detection, classification, prediction, segmentation), dataset size, AI architecture or algorithm, reference standard (e.g., expert orthodontist annotations), primary performance metrics, and whether internal and/or external validation was performed.\u003c/p\u003e\u003cp\u003eRisk of bias in diagnostic accuracy studies was assessed using the QUADAS-2 tool, which evaluates four domains: patient selection, index test, reference standard, and flow/timing [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Prediction model studies were assessed using the PROBAST tool, which covers participants, predictors, outcomes, and analysis domains [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Summary risk-of-bias assessments are presented in the Supplementary QUADAS-2 and PROBAST tables, with study-level details provided separately.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Synthesis methods\u003c/h2\u003e\u003cp\u003eThe included studies displayed substantial heterogeneity in study design (diagnostic accuracy vs prediction models), imaging modalities (2D radiographs, CBCT, panoramic radiographs, intraoral photographs, clinical records), AI architectures, and outcome measures (e.g., landmark error, Dice similarity coefficient, AUC, accuracy, MAE). Because of this variability, quantitative meta-analysis was not feasible.\u003c/p\u003e\u003cp\u003eInstead, a structured narrative synthesis was conducted in accordance with PRISMA 2020 guidance. Studies were grouped by primary AI task (e.g., cephalometric landmark detection, malocclusion classification, extraction decision support, treatment duration prediction, CBCT segmentation), and key performance indicators were summarized within each category.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe PRISMA 2020 flow diagram summarizing study selection is shown in Fig.\u0026nbsp;1 near here (PRISMA 2020 flow diagram of study identification, screening, eligibility, and inclusion).\u003c/p\u003e\u003cp\u003eFigure 1. PRISMA 2020 flow diagram of study identification, screening, eligibility, and inclusion.\u003c/p\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Study selection\u003c/h2\u003e\u003cp\u003eThe database search identified 1,162 records. After removal of duplicates, 1,008 records remained for title and abstract screening. Of these, 854 records were excluded. A total of 154 full-text articles were assessed for eligibility. One hundred and nine full-text articles were excluded for the following main reasons: not orthodontics-related AI studies (n\u0026thinsp;=\u0026thinsp;35), review/editorial/commentary articles (n\u0026thinsp;=\u0026thinsp;28), inappropriate or irrelevant study design (n\u0026thinsp;=\u0026thinsp;20), insufficient diagnostic or treatment-planning detail (n\u0026thinsp;=\u0026thinsp;16), and duplicate publication (n\u0026thinsp;=\u0026thinsp;10). Finally, 45 studies met all inclusion criteria and were included in the qualitative synthesis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Study characteristics\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;1 summarizes the characteristics of the 45 included studies. The majority were retrospective observational studies conducted in academic or specialist orthodontic settings. Common imaging modalities included 2D cephalometric radiographs, CBCT, panoramic radiographs, and intraoral photographs.\u003c/p\u003e\u003cp\u003eThe main AI tasks and applications were:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eCephalometric landmark detection and cephalometric analysis;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eMalocclusion and skeletal pattern classification;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eExtraction-decision support and prediction of orthognathic surgery necessity;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePrediction of treatment duration and treatment outcomes;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eCBCT-based 3D segmentation of dental and skeletal structures.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eSample sizes varied widely, ranging from fewer than 100 images or patients to more than 1,000. CNN-based DL approaches were predominant for image-based tasks, whereas traditional ML algorithms (e.g., random forests, support vector machines, gradient boosting) were more commonly used for structured clinical data and prediction tasks.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;1. Study Characteristics of Included Studies (n\u0026thinsp;=\u0026thinsp;45)\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis PRISMA-guided systematic review demonstrates that AI is no longer a distant prospect but an emerging reality in orthodontic care. Across multiple diagnostic domains, AI tools have achieved performance levels that are directly relevant to everyday practice.\u003c/p\u003e\n\u003cp\u003eFor cephalometric landmark detection, CNN-based models consistently report mean radial errors below 2 mm and high successful detection rates, often comparable to the performance of experienced orthodontists [7–12]. Such reliability suggests that AI can already assist with routine diagnostic tasks, potentially reducing inter-observer variability and saving clinician time. Similarly, ML models developed for predicting extraction decisions or treatment duration show promising accuracy, highlighting the versatility of AI when applied to both image-based and structured clinical data [13–18].\u003c/p\u003e\n\u003cp\u003eSegmentation of CBCT scans has reached a clinically meaningful level of performance, with advanced DL architectures such as nnU-Net and transformer-based models frequently achieving Dice similarity coefficients greater than 0.90 for teeth and alveolar bone [19–22]. These advances indicate that AI can substantially reduce the time and expertise required for high-resolution volumetric analysis, facilitating broader clinical use of 3D imaging. Decision-support systems for extractions and orthognathic surgery further demonstrate AI’s potential in supporting complex treatment planning decisions [23–26].\u003c/p\u003e\n\u003cp\u003eDespite these encouraging results, several important challenges must be addressed before AI can be widely adopted in routine orthodontic care. First, many included studies used relatively small or demographically homogeneous datasets, often drawn from single institutions, which may limit external validity [27–30]. Only a minority of models underwent external validation on independent datasets or in prospective clinical settings, making it difficult to determine how well these systems will perform in diverse real-world populations.\u003c/p\u003e\n\u003cp\u003eSecond, transparency and interpretability remain major concerns. Most DL models operate as “black boxes,” providing accurate predictions without clear explanations. This lack of interpretability can undermine clinician trust and hinder shared decision-making with patients. Explainable AI (XAI) approaches—including saliency maps, attention mechanisms, and feature-importance analyses—are therefore essential to clarify model reasoning and to support responsible clinical use [31–33].\u003c/p\u003e\n\u003cp\u003eThird, broader ethical and practical issues must be carefully considered. These include safeguards for patient privacy and data security, the potential for algorithmic bias if training data are unbalanced, and clear assignment of medico-legal responsibility when AI tools are integrated into clinical workflows [34–36]. Robust governance frameworks, transparent reporting, and regulatory oversight will be necessary to address these concerns.\u003c/p\u003e\n\u003cp\u003eFuture research should prioritize:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eLarge, multicenter, and demographically diverse datasets to improve generalizability and reduce algorithmic bias.\u003c/li\u003e\n \u003cli\u003eStandardized benchmarks and publicly accessible datasets for key orthodontic AI tasks, enabling direct comparison of model performance across studies.\u003c/li\u003e\n \u003cli\u003eProspective clinical studies and implementation research to evaluate how AI tools affect diagnostic accuracy, treatment outcomes, workflow efficiency, and patient-reported outcomes in real clinical settings.\u003c/li\u003e\n \u003cli\u003eAdherence to AI-specific reporting guidelines (e.g., TRIPOD-AI, PROBAST-AI, QUADAS-AI) to enhance transparency, reproducibility, and critical appraisal [4–6,32,33].\u003c/li\u003e\n \u003cli\u003eIntegration of explainable and human-in-the-loop AI systems, in which clinicians and algorithms complement each other, ensuring that orthodontists remain the ultimate decision-makers.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eIn summary, AI is beginning to reshape orthodontics by improving efficiency, reducing inter-observer variability, and enabling more personalized treatment planning. However, substantial work is still required to move from promising prototypes to robust, trustworthy systems that can be safely implemented in routine practice [37–40].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAI currently demonstrates strong performance across multiple orthodontic diagnostic and treatment-planning tasks. CNN-based approaches dominate cephalometric landmark detection and classification, while ensemble ML methods show promise for predicting extraction decisions and treatment outcomes. Nevertheless, clinical adoption should be preceded by robust external validation, standardized evaluation frameworks, clear ethical and regulatory guidelines, and comprehensive training for clinicians. With these safeguards in place, AI can evolve from an auxiliary tool into an integral component of high-quality, patient-centred orthodontic care.\u003c/p\u003e\n"},{"header":"Declarations","content":"\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eNot applicable (no primary data were collected).\u003c/p\u003e\n\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study (extraction sheet, PRISMA checklist, QUADAS-2 and PROBAST assessments) are available from the corresponding author on reasonable request.\u003c/p\u003e\n\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eNo specific funding was received for this work.\u003c/p\u003e\n\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eConceptualization: SMBH, AHBR, RI.\u003c/p\u003e\n\u003cp\u003eMethodology and search strategy: SMBH, AHBR, RH.\u003c/p\u003e\n\u003cp\u003eScreening and data extraction: SMBH, AAM.\u003c/p\u003e\n\u003cp\u003eRisk-of-bias assessment (QUADAS-2 and PROBAST): SMBH, RI, RH.\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; original draft: SMBH.\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; review and editing, and supervision: AHBR, RI, RH.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePage MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWhiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155(8):529\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoons KG, Wolff RF, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies. Ann Intern Med. 2019;170(1):51\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCollins GS, Dhiman P, Navarro CLA, Ma J, Hooft L, Smidt N, et al. Protocols for reporting AI-based diagnostic accuracy studies (TRIPOD-AI, PROBAST-AI). BMJ Open. 2021;11:e048008.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSounderajah V, Ashrafian H, Deo RC, et al. 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Explainable AI in dentistry: a scoping review. J Dent. 2021;110:103664.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJheon AH, et al. Machine learning and orthodontics: a narrative review. Prog Orthod. 2021;22(1):18.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSingh P, et al. Artificial intelligence and big data in orthodontics: challenges and opportunities. Semin Orthod. 2021;27(4):343\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbdi AH, et al. Deep learning in dental radiology: a systematic review. Dentomaxillofac Radiol. 2021;50(4):20200175.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu J, Cruz Rivera S, Moher D, et al. Multi-task deep learning for dental and skeletal classification. Med Image Anal. 2022;76:102313.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBMC Oral Health. About the journal. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bmcoralhealth.biomedcentral.com/about\u003c/span\u003e\u003cspan address=\"https://bmcoralhealth.biomedcentral.com/about\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [Accessed 2025-08-26].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBMC Oral Health. Preparing your manuscript. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bmcoralhealth.biomedcentral.com/submission-guidelines/preparing-your-manuscript\u003c/span\u003e\u003cspan address=\"https://bmcoralhealth.biomedcentral.com/submission-guidelines/preparing-your-manuscript\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [Accessed 2025-08-26].\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","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":"","lastPublishedDoi":"10.21203/rs.3.rs-8108220/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8108220/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eArtificial intelligence (AI) applications in orthodontics are rapidly expanding across diagnosis, image analysis, and treatment planning.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA PRISMA-guided systematic review was conducted. PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar were searched from 2010 to 16 September 2025. Original studies in orthodontics that used AI or machine learning for diagnosis, prediction, image analysis, or treatment planning were eligible. Two reviewers independently screened records, extracted data, and assessed risk of bias using QUADAS-2 for diagnostic accuracy studies and PROBAST for prediction model studies. Owing to heterogeneity in study design, datasets, and outcome metrics, results were synthesized narratively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf 1,162 records identified, 1,008 remained after duplicate removal and were screened by title and abstract. A total of 154 full-text articles were assessed for eligibility, and 45 met the inclusion criteria. Frequent AI tasks included cephalometric landmark detection, malocclusion classification, extraction-decision support, treatment duration prediction, and cone-beam computed tomography (CBCT)-based segmentation. Many studies reported high accuracies for cephalometric landmark detection (mean radial error \u0026lt; 2 mm and successful detection rates \u0026gt; 80%) and malocclusion classification (accuracies \u0026gt; 85%). However, risk-of-bias concerns, particularly in analysis and validation domains, were common, and external validation was infrequent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAI models show promising performance for orthodontic diagnosis and treatment planning and may enhance efficiency and standardization of care. Nevertheless, non-standardized outcome measures, limited external validation, and insufficient reporting of model development and evaluation currently restrict clinical translation. Larger, multicenter datasets, standardized benchmarks, and robust validation—ideally following AI-specific reporting guidelines—are required before routine clinical adoption.\u003c/p\u003e\n\u003cp\u003eRegistration\u003c/p\u003e\n\u003cp\u003ePROSPERO CRD420251134644.\u003c/p\u003e","manuscriptTitle":"The Integration of Artificial Intelligence in Orthodontic Diagnosis and Treatment Planning: A PRISMA-Guided Systematic Review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-16 14:18:34","doi":"10.21203/rs.3.rs-8108220/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":"7dcea1dc-ce91-422f-beb1-15dac1fd91da","owner":[],"postedDate":"December 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-29T10:09:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-16 14:18:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8108220","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8108220","identity":"rs-8108220","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}