Generation of Tooth Replicas by Virtual Segmentation Using Artificial Intelligence

Generation of Tooth Replicas by Virtual Segmentation Using Artificial Intelligence | 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 Research Article Generation of Tooth Replicas by Virtual Segmentation Using Artificial Intelligence Ignacio Pedrinaci, Anita Nasseri, Javier Calatrava, Emilio Couso-Queiruga, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4576625/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 Objectives: The primary aim of this investigation was to validate a method for generating 3D replicas through virtual segmentation, utilizing artificial intelligence (AI) or manual-driven methods, assessing accuracy in terms of volumetric and linear discrepancies. The secondary aims were the assessment of time efficiency with both segmentation methods and the effect of post-processing 3D replicas. Methods: Thirty teeth were scanned through Cone Beam Computed Tomography (CBCT), capturing the region of interest from human subjects. DICOM files underwent segmentation through both AI and manual-driven methods. Replicas were fabricated with a stereolithography 3D printer. After surface scanning of pre-processed replicas and extracted teeth, STL files were superimposed to evaluate linear and volumetric differences using the extracted teeth as the reference. Post-processed replicas were scanned to assess the effect of post-processing on linear and volumetric changes. Results: AI-driven segmentation resulted in statistically significant mean linear and volumetric differences of -0.709mm and -4.70%, respectively. Manual segmentation showed no statistically significant differences in mean linear (-0.463mm) and volumetric (-1.20%) measures. Comparing manual and AI-driven segmentations, showed that AI-driven segmentation displayed mean linear and volumetric differences of -0.329mm and -2.23%, respectively. Additionally, AI segmentation reduced mean time by 21.8 minutes. When comparing post-processed to pre-processed replicas, there was a volumetric reduction of -4.53% and a mean linear difference of -0.151mm. Conclusion: Both segmentation methods achieved acceptable accuracy, with manual segmentation slightly more accurate and AI-driven segmentation more time-efficient. Continuous improvement in AI offers the potential for increased accuracy, efficiency, and broader application in the future. Clinical Significance: Tooth replica generation in the context of tooth autotransplantation therapy may contribute to enhanced success and survival rates. Accurate CBCT-based virtual segmentation and 3D printing technologies are particularly important in the fabrication of 3D replicas. Therefore, it is crucial to assess the accuracy of available techniques and alternatives to demonstrate their reliability and accuracy in the fabrication of tooth replicas. Autotransplantation Computer Aided Manufacturing Digital Dentistry Artificial Intelligence Three-dimensional Printing Stereolithography. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. INTRODUCTION Digital dentistry is a dynamic and rapidly evolving discipline that has revolutionized dentistry. One of its fundamental principles is the process of acquiring and accurately segmenting three-dimensional (3D) images [1]. Virtual segmentation of DICOM data files obtained from Cone Beam Computed Tomography (CBCT) scans provides useful diagnostic tools for enhancing the accuracy and efficiency in dental implant placement, orthodontic planning, disease detection, and computer-aided rapid prototyping (CARP) for creating 3D tooth replicas [2–6]. These CARP replicas may play an important role in therapeutic interventions such as tooth autotransplantation [7]. Tooth autotransplantation is a viable surgical-restorative alternative to replace a missing or hopeless tooth by repositioning an autologous tooth within the same individual [7, 8]. This treatment option is particularly well suited for patients under active alveolar process growth or those with malocclusions where the orthodontic movement of the transplanted tooth is indicated since a successfully transplanted tooth generally maintains a vital periodontium [9, 10]. In the conventional autotransplantation technique, the extracted donor tooth is used to prepare the new recipient site [11]. This method requires multiple fitting attempts and adjustments, increasing the risk of potential damage to the periodontal ligament of the future transplant [7, 8, 12]. Additionally, these procedures prolong the time that the tooth remains outside the oral environment, risking the intervention’s success [7]. In the digital autotransplantation technique, a 3D replica of the donor’s tooth is fabricated based on the virtual segmentation of DICOM files from a previous CBCT scan. The 3D replica is then used as the template to create and adjust the recipient site, thus avoiding any damage to the donor’s tooth and, at the same time, reducing the time the extracted tooth is out of the socket [8, 12, 13]. The use of 3D replicas has demonstrated increased success and survival rates [7, 14, 15]. Nonetheless, to fabricate 3D replicas, it is crucial to establish an accurate method for CBCT virtual segmentation, which may be performed either manually or using artificial intelligence tools (AI) [1, 16]. Manual segmentation relies on the operator's skill and experience, leading to longer segmentation time. AI-driven segmentation is fully automatic based on algorithms, offering a time-efficient alternative [1, 17, 18]. However, it is yet unknown whether AI-driven CBCT segmentation provides the same accuracy as manual segmentation. Therefore, the primary aim of this study was to validate a method for generating 3-D tooth replicas through virtual segmentation, utilizing either artificial intelligence (AI) or manual-driven methods. This validation was accomplished by assessing the method's accuracy regarding volumetric and linear discrepancies. As secondary outcomes, the time spent during the tooth segmentation procedures with either method and the effect of post-processing 3D tooth replicas were assessed. Previous studies have shown a statistically significant difference (SSD) in linear and volumetric measurements when comparing replicas made with manual segmentation versus the original teeth [19]. Therefore, the null hypothesis of this investigation will be that there are no differences in the replica’s linear and volumetric measurements between AI and manual-driven methods. 2. MATERIALS AND METHODS 2.1. Experimental design, setting, and timeframe. This clinical investigation is in compliance with the Checking for Reporting In-Vitro studies (CRIS) [20]. All the therapeutic and follow-up interventions in this study were carried out in the Specialization Postgraduate Implant Clinic of the Harvard School of Dental Medicine between September 2021 and December 2023. 2.2. Ethical approval, patient recruitment, and extraction protocol After obtaining approval from the Institutional Review Board (IRB21-1687), selected patients fulfilling the defined inclusion criteria were informed on the purpose of the study on their extracted teeth by one of the researchers (I.P., A.N.). Eligible patients included in this study signed the informed consent. The inclusion criteria were: 1) The patient’s ability to sign an informed consent form for enrollment in the study and 2) Any tooth suitable for extraction with a pre-operative CBCT scan taken no more than 60 days before the intervention. The exclusion criteria included teeth with carious lesions, cracked or fractured, presence of fixed dental prosthesis, endodontic treatment, or any restorative material that could cause scattering or might interfere with the CBCT virtual segmentation procedure. All extractions were conducted as minimally traumatic as possible to avoid any damage to the tooth or adjacent anatomical structures. Following extractions, teeth were labeled and gently cleansed with water to remove any attached soft tissue and subsequently immersed for 30 min in a 1:10 solution of bleach for decontamination. 2.3. Sample preparation 2.3.1. CBCT acquisition and segmentation Preoperative CBCT scans were obtained at Harvard School of Dental Medicine using the Veraview X800 MORITA device (MORITA Inc, Kyoto, Japan) for orthodontic, surgical or implant planning diagnosis. Standardized settings were utilized for all CBCT scans, with an 80x40 field of view (FOV), 100 kV, 7 mA, and a resolution of 1.0 mm. Manual segmentations were conducted by a single investigator (AN) following a standardized protocol. The CBCT scans were exported to the Blue-Sky Bio software (Blue Sky Bio, LLC, Libertyville, Illinois), and using the "Advanced Tooth Segmentation" tool of this software, segmentations were carried out in the area of interest. Teeth were manually outlined layer by layer using the lasso tool, and subsequent refinements were achieved utilizing the brush tool. Fifteen slices, with a minimum density grey values threshold of 900, and the “Smooth” function were applied to all manual segmentations. Then, the resulting 3D replicas were saved in standard tessellation language (STL) files ( Fig. 1 ). AI-driven segmentations were carried out using the Diagnocat® software (Diagnocat, San Francisco, California) by a single investigator (I.P.). This software uses a Convolutional Neural Networks (CNN) algorithm following a progressive coarse-to-fine framework for resolution analysis. Then, the resulting 3D replicas were exported from Diagnocat® to STL files. 2.3.2 3D printing All 3D replicas were printed with a 3D printer (Formlabs Form 3B+, Formlabs, Somerville, Massachusetts), using Low Force Stereolithography (LFS) technology. Temporary crown-bridge (CB) resin (Formlabs, Somerville, Massachusetts) was used as the printing material. The pre-processed replicas underwent a thorough washing procedure in isopropyl alcohol for 3 minutes following manufacturer recommendations (Fig. 2 ). 2.3.3 Post-processing Following the manufacturer’s recommendation, replicas with the supports still attached were first cured in the Form Cure (Formlabs, Somerville, Massachusetts) at 60°C (140°F) for 20 minutes. After the first curing, supports and rafts (3–5 supports, 0.70mm diameter, only on the occlusal surface) were manually removed, and replicas were carefully sandblasted to refine their surface quality. Finally, the replicas underwent a second curing process at 60°C (140°F) for 20 minutes. 2.3.4. Surface scanning The extracted teeth and their corresponding 3D pre- and post-processed replicas were digitally scanned using a laboratory scanner (3Shape E Series Lab Scanner, 3Shape, Copenhagen, Denmark). The teeth were secured on the scanner's platform by the root, and the coronal portion of each tooth was scanned first. Next, the teeth were inverted to scan the apical portion. The coronal and apical scans were then superimposed and aligned to create a complete 3D surface model of each tooth, which was then saved as an STL file. An overview of the methodology can be found in Fig. 3 . 2.4. Outcome measurements 2.4.1. Volumetric and linear assessment For the volumetric measurements, STL files were analyzed by a single examiner (E.C.Q) using a previously published methodology with a specialized software package (Geomagic Control X, 3D Systems, Rock Hill, SC, USA) [21]. The entire projected volume was measured in mm3 ( Fig. 4 and Fig. 5 ). The examiner was trained and calibrated by conducting a series of 10 separate volumetric assessments in duplicate. To assess linear differences, STL files were exported to a software package (Autodesk Meshmixer, San Francisco, California), superimposed, aligned, and compared with the STL files from the extracted tooth. These measurements along the X, Y, and Z axes were carried out by a single examiner (A.N.) using the software's "Unit/Dimensions" analysis tool ( Fig. 4 and Fig. 5 ). 2.5. Statistical analysis Sample size was calculated based on data from the study of Lee et al. 2022 [19], forecasting a mean difference of 0.38mm in linear measurements using 3-D printed resin replicas (Formlabs®) [18] with a standard deviation of ± 0.22 for the mean linear measurement. Based on an alpha error of 5%, a power of 85%, and a two-sided (equivalence) test, 12 specimens were deemed necessary in this study. Statistical analyses were done using each extracted tooth as the statistical unit. Outcome variables are presented through descriptive statistics, expressing continuous variables as means, standard deviations (SD), and confidence intervals of 95%, while categorical variables are expressed as percentages (%). Data normality was calculated using a Shapiro-Wilk test. The primary outcome variable was the volumetric and linear changes between the final post-processed 3-D printed replicas with the original extracted tooth, with either segmentation method used (i.e., manual or AI segmentation). Differences were evaluated using the 2-sided paired sample Student’s T-test, with a p-value of p ≤ 0.05 as statistically significant. When data did not meet normality criteria, a Wilcoxon signed-rank test was used. Binary categorical data were evaluated with a Chi-squared test. Intraclass correlation coefficients (ICCs) were also calculated for each of these comparisons to evaluate the correlation between the volumetric measurements of different protocols. Secondary outcomes include time efficiency between different segmentation methods, as well as volumetric and linear changes due to post-processing 3D replicas. Continuous variables used paired Student t-test or Wilcoxon signed-rank test depending on the normality of data, as well as ICCs. All data analyses were performed with SPSS version 21.0 software (Chicago, IL, USA). 3. RESULTS 3.1. Sample Characteristics The final sample consisted of 30 extracted teeth from 8 patients (5 males and 3 females) with ages ranging between 13 to 55 years (mean age 32.25, SD 14.69). The extracted teeth comprised multi-rooted teeth (1 mandibular and 3 maxillary molars), and 26 were single-rooted teeth (15 premolars (7 mandibular and 8 maxillary), 4 canines (2 mandibular and 2 maxillary), 5 lateral incisors (2 mandibular and 3 maxillary), and two central incisors (1 mandibular and 1 maxillary)). (Table 1 ). Table 1 Sample characteristics Total (Patients) 8 (%) Male 5 (62.5) Female 3 (37.5) Age ≤ 20 2 (25) ≤ 35 3 (37.5) 35–55 3 (37.5) Total (Teeth) 30 Molar 4 (13.3) Premolar 15 (50) Canine 4 (13.3) Lateral Incisor 5 (16.7) Central Incisor 2 (6.7) Number of Roots Multi (Three) 1 (3.3) Multi (Two) 3 (10) Single 26 (86.7) 3.2. Intra-examiner Reliability The calibration exercise provided a high intra-examiner agreement, with a strong ICC ranging from 0.97–0.99 for the volumetric and linear analysis (Table 2 ). Table 2 Intraclass correlation analysis of volumetric and linear measurements. Volumetric Linear (Y) ICC p-value ICC p-value Pair 1 Extracted 0.997 < .001 0.987 0.001 Vs. Scan_Manual_Postprocessed Pair 2 Extracted 0.994 < .001 0.971 0.003 Vs. Scan_AI_Postprocessed Pair 3 Extracted 0.997 < .001 0.995 < .001 Vs. Segmentation_Manual Pair 4 Extracted 0.998 < .001 0.975 < .001 Vs. Segmentation_AI Pair 5 Segmentation_AI 0.997 < .001 0.983 < .001 Vs. Segmentation_Manual Pair 6 Preprocessed_pooled 0.996 0.004 0.987 < .001 Vs. Postprocessed_pooled Table 3 Comparative analysis of volumetric and linear measurements of STL files from virtual segmentation replicas (A) Volumetric Linear (Y) Mean SD 95% CI p-value Mean SD 95% CI p-value Pair 1 Extracted 5.651 19.469 [-1.62, 12.92] 0.123 0.463 0.335 [0.34, 0.59] < .001 Vs. Scan_Manual_Postprocessed Pair 2 Extracted 22.128 14.917 [16.56, 27.70] < .001 0.709 0.491 [0. 53, 0.89] < .001 Vs. Scan_AI_Postprocessed Pair 3 Extracted 1.766 0.566 [-5.84, 9.37] 0.638 0.221 0.281 [0.12, 0.33] < .001 Vs. Segmentation_Manual Pair 4 Extracted 12.232 11.334 [7.99, 16.46] < .001 0.550 0.574 [0. 34, 0.76] < .001 Vs. Segmentation_AI Pair 5 Segmentation_AI 10.466 17.354 [3.99, 16.95] 0.003 0.329 0.566 [0. 12, .054] 0.003 Vs. Segmentation_Manual (B) Volumetric Linear (Y) Mean SD 95% CI p-value Mean SD 95% CI p-value Pair 1 Scan_AI_Preprocessed 21.419 6.523 [18.98, 23.85] < .001 0.210 0.777 [-.008, 0.50] 0.150 Vs. Scan_AI_Postprocessed Pair 2 Scan_Manual_Preprocessed 21.975 7.232 [19.27, 24.68] < .001 0.093 0.190 [0.02, 0.16] 0.012 Vs. Scan_Manual_Postprocessed Pair 3 Preprocessed_pooled 21.697 6.833 [19.93, 23.46] < .001 0.151 0.564 [0.01, 0.30] 0.042 Vs. Postprocessed_pooled (A) AI-driven and manually driven analysis. (B) Post-processed effect analysis 3.3. Accuracy of the comprehensive Computer-Aided Rapid Prototyping (CARP) process using either manual segmentation or AI-driven segmentation 3.3.1 Linear measurements A mean linear difference of 0.463mm (SD 0.335) was observed when comparing post-processed 3-D replicas obtained by manual segmentation with the corresponding extracted teeth. These differences were statistically significant (p < 0.001) (Table 3 A). Similarly, the mean linear difference when comparing post-processed 3-D replicas obtained by AI segmentation with the corresponding extracted teeth was 0.709mm (SD 0.491), with these differences being statistically significant (p < 0.001) (Table 3 A). Direct comparison between the 3D replicas obtained from manual and AI segmentation resulted in a mean linear difference of 0.221mm (SD 0.281). These differences were statistically significant (p < 0.001) (Table 3 A). 3.3.2. Volumetric measurements A mean volumetric difference of 5.651mm 3 (SD 19.469) was obtained between the manually segmented 3D-printed replicas and the extracted teeth, corresponding to a 1.20% volume reduction. These differences, however, were not statistically significant (p = 0.123) (Table 3 A and Table 4). Conversely, the mean volumetric difference when comparing replicas obtained by AI segmentation with the corresponding extracted teeth was 22.128mm³ (SD 14.917), corresponding to a -4.70% volume reduction. These differences were statistically significant (p < 0.001) (Table 3 A and Table 4). Direct comparison between STL files (3D surfaces) generated from AI-driven and manual segmentation found an overall mean volumetric difference of 10.466mm³ (SD 17.354, p = 0.003), equivalent to a -2.23% change in volume (Table 3 A and Table 4). Replicas from AI-driven segmentation were smaller than those originating from manual segmentation Table.4. Descriptive volumetric comparison. Volumetric Mean Mean diff % diff in Vol Pair 1 Extracted tooth 470.782 5.651 -1.20 Vs. Scan_Manual_Postprocessed 3D printed replica 465.132 Pair 2 Extracted tooth 470.782 22.128 -4.70 Vs. Scan_AI_Postprocessed 3D printed replica 448.654 Pair 3 Extracted tooth 470.782 1.766 -0.38 Vs. Segmentation_Manual 469.016 Pair 4 Extracted tooth 470.782 12.232 -2.60 Vs. Segmentation_AI 458.550 Pair 5 Segmentation_AI 458.550 10.466 -2.23 Vs. Segmentation_Manual 469.016 Volumetric Mean Mean diff % diff in Vol Pair 1 Scan_AI_Preprocessed 470.073 21.419 -4.56 Vs. Scan_AI_Postprocessed 448.654 Pair 2 Scan_Manual_Preprocessed 487.107 21.975 -4.51 Vs. Scan_Manual_Postprocessed 465.132 Pair 3 Preprocessed_pooled 478.590 21.697 -4.53 Vs. Postprocessed_pooled 456.893 Comparisons for pairs 1, 2, 4, and 5 were conducted by establishing the extracted teeth as a reference for comparing the size of the replicas. In the case of pair 3, AI-segmented 3D replicas were compared against manually segmented replicas, with the manual group serving as the reference. For all post-processing groups, pre-processed replicas were used as the reference for comparison. ** “Segmentation manual” and “Segmentation IA” are digital files (STL) that have yet to be printed. ** “Pre-processed” and “Post-processed” represent digital files (STL) obtained after scanning the 3D-printed replicas. 3.4. Accuracy of post-processed replicas in comparison to preprocessed replicas Comparative analyses were conducted by separately comparing all the teeth replicas generated through AI-driven segmentation and manual segmentation, pre- and post-processing. Subsequently, data from all replicas were pooled together to assess the overall impact of post-processing on both volumetric and linear measurements. 3.4.1. Linear measurements Analyzing the pre- and post-processed replicas from AI-driven segmentation showed a mean linear difference of 0.210mm (SD 0.777), demonstrating no statistical significance (p = 0.15). In contrast, the same comparison for the manual segmentation counterpart group indicated a statistically significant mean linear difference of 0.093mm (SD 0.190, p = 0.012). Combining both groups, an overall mean linear difference of 0.151mm (SD 0.564) was observed, and this difference was statistically significant (p = 0.042) (Table 3 B). 3.4.2. Volumetric measurements When comparing the pre and post-processed replicas obtained from AI-driven segmentation, there was a mean volumetric difference of 21.419mm³ (SD 6.523),-4.56% volume reduction. The same comparison for the replicas obtained from manual segmentation showed a mean volumetric difference of 21.975mm³ (SD 7.232), -4.51% volume reduction. When both groups (AI and manually segmented) were pooled together, an overall mean volumetric difference of 21.697mm³ (SD 6.833), -4.53% volume reduction, was found between pre-processed and post-processed replicas (Table.4). Importantly, all groups demonstrated statistically significant results (p < 0.001) (Table.3B). Post-processed replicas were generally smaller than the pre-processed ones. 3.5. Comparison of segmentation times between manual and AI-driven methods The average time required for manual segmentation of 30 teeth was 23.97 minutes, and the average time required for AI-driven segmentation of the same teeth was reported to be 2.1 minutes. 4. DISCUSSION The application of CARP in tooth autotransplantation involves multiple steps, which include CBCT acquisition and segmentation, 3D printing, and subsequent post-processing of 3D-printed replicas. The accuracy of each step may have a substantial impact on the overall accuracy of the final 3D replica. This study validates the entirety of the CARP process, assessing the accuracy of manual and AI tooth segmentation and its resulting 3D-printed replicas compared with the reference extracted teeth. We observed that both methods were reliable and suitable for the fabrication of 3D tooth replicas, as the observed statistically significant differences between methods can be considered non-clinically significant. However, time-efficiency analysis demonstrates a reduction in time of 21 minutes for the AI-driven method. Manual segmentation is a well-established method to obtain tooth replicas, and previous studies have validated its accuracy, considering it to be the gold standard [2]. However, it is a time-consuming process that demands training and experience and relies on the interpretation skills of the operator. The average time for manual segmentation of each tooth in this investigation was 23.97 minutes. Nonetheless, other studies, such as Lee et al. [22], reported an average time of 15 minutes per tooth. Another study on manual segmentations of single and double-rooted teeth reported an average time of 6.6 minutes. Interestingly, AI-driven segmentation resulted in a 12.5-fold reduced time compared to manual segmentation [23]. Considering that manual segmentation involves the investigator selecting and individually outlining multiple image slices, the reported time in different studies can vary significantly based on the type of teeth, the number of slices selected, and the precision of the outlining process [1, 22, 24]. This fact was noted in this study considering the higher standard deviations in the manual vs. the AI method. Several AI algorithms and deep learning models have recently been developed to carry out a fully automatic tooth segmentation more efficiently within a few minutes [1, 23, 25–27]. One of the most effective models is the CNN, which has been integrated into the software used for AI-driven segmentation in this study [1, 28]. Comprising multilayer neural networks, CNN algorithms excel in identifying visual patterns quickly and with minimal pre-processing requirements [1, 28]. However, these models have certain limitations, and recent review studies have underscored the necessity for validating their accuracy and reliability [1]. Several challenges noted in other studies involve the segmentation of intricate root anatomy and apices, supernumerary and impacted teeth, especially third molars, and cases of crowding [1, 17, 23, 25, 29]. These factors may reasonably account for our findings regarding the lower accuracy of AI-driven segmentation versus the manual segmentation group. This finding may also be explained by the fact that manual segmentation was performed by the same experienced operator under ideal and controlled circumstances. This study demonstrated a reduction in volume (-0.38 to -2.6% for manual and AI segmentation, respectively) when comparing the virtual files obtained after segmentation and the scanned tooth. Interestingly, volumetric and linear analyses of post-processed replicas showed a smaller trend compared to the extracted teeth (-4.53% volume reduction). These findings could be attributed to resin shrinkage during post-curing. Similarly, a study by Lee and Kim also reported that 3D replicas from CT images were generally smaller than the actual teeth [30]. Their results revealed that, on average, the 3D images of donor teeth were − 0.149 mm smaller than the actual teeth, and the 3D replicas were, on average, -0.067 mm smaller than their corresponding 3D images. Despite the observed size discrepancy, it is noteworthy that this error may be clinically acceptable for the application of these replicas in the context of tooth autotransplantation therapy. Recognizing the benefits of utilizing 3D replicas to reduce extraoral time and minimize damage to the periodontal ligament, the size discrepancy can be clinically manageable [7]. This factor, coupled together with the volume reduction after virtual segmentation reported in this study, can be taken into consideration during the planning phase. Therefore, clinicians should be aware that the surgical area should be minimally overprepared based on the 3D replica and to allow some physical space for blood clot formation and establishment around the roots of the autotransplanted tooth. Another important aspect to consider is that, while this study reports AI-driven segmentation as less accurate than the manual method, it was notably more time-efficient and less dependent on operator input, demonstrated by a higher SD on the manual-segmentation group. Continuous advancements in AI algorithms and deep learning models hold the promise of significant improvements in tooth segmentation software. By harnessing the potential for ongoing training and improvement of AI systems, there is a clear path towards achieving higher accuracy and efficiency in digital segmentation processes. Efficiency is a significant factor in treatment planning and the practice of modern dentistry. In the context of autotransplantation procedures, earlier studies indicated that using 3D replicas can significantly enhance the success and efficiency of surgery. Shahbazian et al. and Verweij et al., reported extra-oral times of less than 1 minute when 3D replicas were employed and an overall significant reduction in procedural time [7, 8, 31, 32]. If AI can streamline the treatment planning phase by reducing the time and effort required for tooth segmentation while maintaining an acceptable level of accuracy, it holds the potential to be a promising tool for enhancing the overall efficiency of surgical treatment planning. The relevance of this investigation lies in a direct evaluation of the accuracy of AI-driven tooth segmentation, both in terms of volumetric and linear data. However, this study also presents some limitations that should be acknowledged. Firstly, the strict inclusion criteria and consistent use of the same CBCT machine, parameters, and standardized operator enhance the study's internal validity but also make it challenging to extrapolate these findings to other protocols. Secondly, only one software for AI-driven segmentation has been tested, as well as 3D printing workflow, and the reported accuracy may not apply to other approaches utilizing different technologies. Lastly, despite conducting a sample size calculation and achieving high power, further studies with larger sample sizes are recommended to investigate the influence of multi-radicular teeth, furcation areas, and complex anatomy on the segmentation process, as well as the relationship between the time dedicated to manual segmentation and its final accuracy. 5. CONCLUSIONS Within the limitations of this study, the following conclusions can be inferred: AI-driven and manual virtual tooth segmentation methods are reliable and suitable for obtaining 3D tooth replicas. AI-driven tooth segmentation proved to be more time-efficient and independent of the operator's experience. Post-processing 3D-printed tooth replicas showed consistently reduced dimensions. Declarations Authors’ contributions: IP (Concept/Design, Data analysis/interpretation, Critical revision of article, Data collection, Approval of article); I.P, A.N, J.C, E.C.Q, M.S (Data analysis/interpretation, Critical revision of article, Data collection, Writing, Approval of article), W.V.G, G.G (Critical revision of article, Approval of article). All authors critically revised the manuscript, gave final approval, and agreed to be accountable for all aspects of the scientific work. Conflict of interest: The authors have no conflicts of interest to report pertaining to the conduction of this study. Data availability statement: The data that support the findings of this study are available from the corresponding author upon reasonable request. Ethics approval statement: This study was approved by the Harvard of Dental Medicine Institutional Review Board (IRB21-1687). Funding statement: No financial support or sponsorship was received for the conduction of this study. 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J Orthod 44:287-293. doi: 10.1080/14653125.2017.1371960 Lucas‐Taulé E, Llaquet M, Muñoz‐Peñalver J, Nart J, Hernández‐Alfaro F and Gargallo‐Albiol J (2021) Mid‐term outcomes and periodontal prognostic factors of autotransplanted third molars: A retrospective cohort study. Journal of periodontology (1970) 92:1776-1787. doi: 10.1002/JPER.21-0074 Zanjani FG, Pourtaherian A, Zinger S, Moin DA, Claessen F, Cherici T, Parinussa S and de With PHN (2021) Mask-MCNet: Tooth instance segmentation in 3D point clouds of intra-oral scans. Neurocomputing (Amsterdam) 453:286-298. doi: 10.1016/j.neucom.2020.06.145 Gardiyanoğlu E, Ünsal G, Akkaya N, Aksoy S and Orhan K (2023) Automatic Segmentation of Teeth, Crown-Bridge Restorations, Dental Implants, Restorative Fillings, Dental Caries, Residual Roots, and Root Canal Fillings on Orthopantomographs: Convenience and Pitfalls. Diagnostics (Basel) 13:1487. doi: 10.3390/diagnostics13081487 Vinayahalingam S, Kempers S, Schoep J, Hsu T-MH, Moin DA, van Ginneken B, Flügge T, Hanisch M and Xi T (2023) Intra-oral scan segmentation using deep learning. BMC oral health 23:1-643. doi: 10.1186/s12903-023-03362-8 Lee CKJ, Foong KWC, Sim YF and Chew MT (2022) Evaluation of the accuracy of cone beam computed tomography (CBCT) generated tooth replicas with application in autotransplantation. J Dent 117:103908. doi: 10.1016/j.jdent.2021.103908 Krithikadatta J, Gopikrishna V and Datta M (2014) CRIS Guidelines (Checklist for Reporting In-vitro Studies): A concept note on the need for standardized guidelines for improving quality and transparency in reporting in-vitro studies in experimental dental research. Journal of conservative dentistry 17:301-304. doi: 10.4103/0972-0707.136338 Couso-Queiruga E, Ahmad U, Elgendy H, Barwacz C, Gonzalez-Martin O and Avila-Ortiz G (2021) Characterization of Extraction Sockets by Indirect Digital Root Analysis. The International journal of periodontics & restorative dentistry 41:141-148. doi: 10.11607/prd.4969 Lee S-C, Hwang H-S and Lee KC (2022) Accuracy of deep learning-based integrated tooth models by merging intraoral scans and CBCT scans for 3D evaluation of root position during orthodontic treatment. Progress in orthodontics 23:15-15. doi: 10.1186/s40510-022-00410-x Lahoud P, EzEldeen M, Beznik T, Willems H, Leite A, Van Gerven A and Jacobs R (2021) Artificial Intelligence for Fast and Accurate 3-Dimensional Tooth Segmentation on Cone-beam Computed Tomography. Journal of endodontics 47:827-835. doi: 10.1016/j.joen.2020.12.020 Al-Ubaydi AS and Al-Groosh D (2023) The Validity and Reliability of Automatic Tooth Segmentation Generated Using Artificial Intelligence. TheScientificWorld 2023:5933003-11. doi: 10.1155/2023/5933003 Gan Y, Xia Z, Xiong J, Zhao Q, Hu Y and Zhang J (2015) Toward accurate tooth segmentation from computed tomography images using a hybrid level set model. Medical physics (Lancaster) 42:14-27. doi: 10.1118/1.4901521 Cui Z, Fang Y, Mei L, Zhang B, Yu B, Liu J, Jiang C, Sun Y, Ma L, Huang J, Liu Y, Zhao Y, Lian C, Ding Z, Zhu M and Shen D (2022) A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images. Nature communications 13:2096-2096. doi: 10.1038/s41467-022-29637-2 Jang TJ, Kim KC, Cho HC and Seo JK (2022) A fully automated method for 3D individual tooth identification and segmentation in dental CBCT. IEEE transactions on pattern analysis and machine intelligence 44:1-1. doi: 10.1109/TPAMI.2021.3086072 Ezhov M, Zakirov A and Gusarev M Coarse-to-fine volumetric segmentation of teeth in cone-beam ct. Book title. IEEE, Hao J, Liao W, Zhang YL, Peng J, Zhao Z, Chen Z, Zhou BW, Feng Y, Fang B, Liu ZZ and Zhao ZH (2022) Toward Clinically Applicable 3-Dimensional Tooth Segmentation via Deep Learning. Journal of dental research 101:304-311. doi: 10.1177/00220345211040459 Lee S-J and Kim E-S (2012) Minimizing the extra-oral time in autogeneous tooth transplantation: use of computer-aided rapid prototyping (CARP) as a duplicate model tooth. Restorative dentistry & endodontics 37:136-141. Verweij JP, Moin DA, Mensink G, Nijkamp P, Wismeijer D and Merkesteyn JPRv (2016) Autotransplantation of Premolars With a 3-Dimensional Printed Titanium Replica of the Donor Tooth Functioning as a Surgical Guide: Proof of Concept. Journal of Oral and Maxillofacial Surgery 74:1114-1119. doi: 10.1016/j.joms.2016.01.030 Shahbazian MDDSP, Jacobs RDDSP, Wyatt JDDSM, Denys DDDSM, Lambrichts IDDSP, Vinckier FDDSP and Willems GDDSP (2013) Validation of the cone beam computed tomography–based stereolithographic surgical guide aiding autotransplantation of teeth: clinical case–control study. ORAL SURGERY ORAL MEDICINE ORAL PATHOLOGY ORAL RADIOLOGY 115:667-675. doi: 10.1016/j.oooo.2013.01.025 Additional Declarations No competing interests reported. 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. 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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-4576625","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":325081777,"identity":"8b491dc9-8f65-44d7-8171-24e7c9f87ce1","order_by":0,"name":"Ignacio Pedrinaci","email":"data:image/png;base64,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","orcid":"","institution":"Harvard School of Dental Medicine","correspondingAuthor":true,"prefix":"","firstName":"Ignacio","middleName":"","lastName":"Pedrinaci","suffix":""},{"id":325081778,"identity":"1d5eacf5-0b65-4245-a243-5a6eee155f09","order_by":1,"name":"Anita Nasseri","email":"","orcid":"","institution":"Harvard School of Dental Medicine","correspondingAuthor":false,"prefix":"","firstName":"Anita","middleName":"","lastName":"Nasseri","suffix":""},{"id":325081779,"identity":"d93de511-c7f8-4176-be5e-eae321a05e79","order_by":2,"name":"Javier Calatrava","email":"","orcid":"","institution":"Complutense University of Madrid","correspondingAuthor":false,"prefix":"","firstName":"Javier","middleName":"","lastName":"Calatrava","suffix":""},{"id":325081780,"identity":"2ea72cfd-fab3-4ab8-84d5-7ea6200968f0","order_by":3,"name":"Emilio Couso-Queiruga","email":"","orcid":"","institution":"University of Bern","correspondingAuthor":false,"prefix":"","firstName":"Emilio","middleName":"","lastName":"Couso-Queiruga","suffix":""},{"id":325081781,"identity":"6f084df4-be46-4495-a21c-967aabaa3e85","order_by":4,"name":"William V. Giannobile","email":"","orcid":"","institution":"Harvard School of Dental Medicine","correspondingAuthor":false,"prefix":"","firstName":"William","middleName":"V.","lastName":"Giannobile","suffix":""},{"id":325081782,"identity":"338b5d6f-ed08-4cb9-96ff-eb84f2578ab8","order_by":5,"name":"German O. Gallucci","email":"","orcid":"","institution":"Harvard School of Dental Medicine","correspondingAuthor":false,"prefix":"","firstName":"German","middleName":"O.","lastName":"Gallucci","suffix":""},{"id":325081783,"identity":"004e05cd-73b2-47df-9d52-4703df255f80","order_by":6,"name":"Mariano Sanz","email":"","orcid":"","institution":"Complutense University of Madrid","correspondingAuthor":false,"prefix":"","firstName":"Mariano","middleName":"","lastName":"Sanz","suffix":""}],"badges":[],"createdAt":"2024-06-13 13:49:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4576625/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4576625/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60069730,"identity":"70f9aba3-90a3-474f-a1ca-c6a5860101df","added_by":"auto","created_at":"2024-07-11 10:40:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":291995,"visible":true,"origin":"","legend":"\u003cp\u003eManual segmentation of a representative tooth from CBCT data.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4576625/v1/76431527cc105be4005e63ed.png"},{"id":60070687,"identity":"0ff01b35-3fda-491e-ae58-002860688571","added_by":"auto","created_at":"2024-07-11 10:56:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":73191,"visible":true,"origin":"","legend":"\u003cp\u003eSide-by-side views of the extracted tooth next (A) to its corresponding 3D printed-replica from virtual segmentation (B).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4576625/v1/b54d2d8887374e15f4415704.png"},{"id":60069727,"identity":"b64c2795-a238-4235-9101-991b5e6f2f28","added_by":"auto","created_at":"2024-07-11 10:40:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":48135,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of methodology. Using the obtained STL files, comparisons were made between E vs M, E vs A, E vs M2, E vs A2, and M vs A groups to assess segmentation accuracy; and effect of post-processing was assessed with comparisons between M1 vs M2 and A1 vs A2 groups. Time required for both manual and AI segmentation was recorded.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4576625/v1/ee7e84b2a141e850c283ce0f.png"},{"id":60069731,"identity":"53d99bac-39da-4910-a0ed-83ab6946a6d6","added_by":"auto","created_at":"2024-07-11 10:40:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":59698,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Paired superimposition of the STL files showing discrepancies between files as represented by 3D comparison color-map. (B) Paired superimposition of the STL files for linear analysis.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4576625/v1/daf3383f30a0ca909034605d.png"},{"id":60070195,"identity":"9b094c3d-60de-438a-a911-61e3159d8074","added_by":"auto","created_at":"2024-07-11 10:48:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":89259,"visible":true,"origin":"","legend":"\u003cp\u003eExample of\u003cstrong\u003e \u003c/strong\u003ethe methodology followed. Left: AI segmentation analysis and Right: manual segmentation analysis. Each row, from left to right: Geomagic superimposition for volumetric analysis (3D color map comparison); Meshmixer superimposition for linear analysis (dark grey); SLT file of extraorally scanned tooth; STL file of (IA or Manual) segmented tooth.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4576625/v1/ec987f607b372672d7ca394e.png"},{"id":60588301,"identity":"569acf2c-ea99-466d-8fcd-e2160366f3df","added_by":"auto","created_at":"2024-07-18 13:59:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1480340,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4576625/v1/806ca9c5-11b8-42af-9fd9-93bc8dd22f2b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Generation of Tooth Replicas by Virtual Segmentation Using Artificial Intelligence","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eDigital dentistry is a dynamic and rapidly evolving discipline that has revolutionized dentistry. One of its fundamental principles is the process of acquiring and accurately segmenting three-dimensional (3D) images [1]. Virtual segmentation of DICOM data files obtained from Cone Beam Computed Tomography (CBCT) scans provides useful diagnostic tools for enhancing the accuracy and efficiency in dental implant placement, orthodontic planning, disease detection, and computer-aided rapid prototyping (CARP) for creating 3D tooth replicas [2\u0026ndash;6]. These CARP replicas may play an important role in therapeutic interventions such as tooth autotransplantation [7].\u003c/p\u003e \u003cp\u003eTooth autotransplantation is a viable surgical-restorative alternative to replace a missing or hopeless tooth by repositioning an autologous tooth within the same individual [7, 8]. This treatment option is particularly well suited for patients under active alveolar process growth or those with malocclusions where the orthodontic movement of the transplanted tooth is indicated since a successfully transplanted tooth generally maintains a vital periodontium [9, 10]. In the conventional autotransplantation technique, the extracted donor tooth is used to prepare the new recipient site [11]. This method requires multiple fitting attempts and adjustments, increasing the risk of potential damage to the periodontal ligament of the future transplant [7, 8, 12]. Additionally, these procedures prolong the time that the tooth remains outside the oral environment, risking the intervention\u0026rsquo;s success [7].\u003c/p\u003e \u003cp\u003eIn the digital autotransplantation technique, a 3D replica of the donor\u0026rsquo;s tooth is fabricated based on the virtual segmentation of DICOM files from a previous CBCT scan. The 3D replica is then used as the template to create and adjust the recipient site, thus avoiding any damage to the donor\u0026rsquo;s tooth and, at the same time, reducing the time the extracted tooth is out of the socket [8, 12, 13]. The use of 3D replicas has demonstrated increased success and survival rates [7, 14, 15]. Nonetheless, to fabricate 3D replicas, it is crucial to establish an accurate method for CBCT virtual segmentation, which may be performed either manually or using artificial intelligence tools (AI) [1, 16]. Manual segmentation relies on the operator's skill and experience, leading to longer segmentation time. AI-driven segmentation is fully automatic based on algorithms, offering a time-efficient alternative [1, 17, 18]. However, it is yet unknown whether AI-driven CBCT segmentation provides the same accuracy as manual segmentation.\u003c/p\u003e \u003cp\u003eTherefore, the primary aim of this study was to validate a method for generating 3-D tooth replicas through virtual segmentation, utilizing either artificial intelligence (AI) or manual-driven methods. This validation was accomplished by assessing the method's accuracy regarding volumetric and linear discrepancies. As secondary outcomes, the time spent during the tooth segmentation procedures with either method and the effect of post-processing 3D tooth replicas were assessed. Previous studies have shown a statistically significant difference (SSD) in linear and volumetric measurements when comparing replicas made with manual segmentation versus the original teeth [19]. Therefore, the null hypothesis of this investigation will be that there are no differences in the replica\u0026rsquo;s linear and volumetric measurements between AI and manual-driven methods.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Experimental design, setting, and timeframe.\u003c/h2\u003e \u003cp\u003eThis clinical investigation is in compliance with the Checking for Reporting In-Vitro studies (CRIS) [20]. All the therapeutic and follow-up interventions in this study were carried out in the Specialization Postgraduate Implant Clinic of the Harvard School of Dental Medicine between September 2021 and December 2023.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.2. Ethical approval, patient recruitment, and extraction protocol\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAfter obtaining approval from the Institutional Review Board (IRB21-1687), selected patients fulfilling the defined inclusion criteria were informed on the purpose of the study on their extracted teeth by one of the researchers (I.P., A.N.). Eligible patients included in this study signed the informed consent.\u003c/p\u003e \u003cp\u003eThe inclusion criteria were: 1) The patient\u0026rsquo;s ability to sign an informed consent form for enrollment in the study and 2) Any tooth suitable for extraction with a pre-operative CBCT scan taken no more than 60 days before the intervention. The exclusion criteria included teeth with carious lesions, cracked or fractured, presence of fixed dental prosthesis, endodontic treatment, or any restorative material that could cause scattering or might interfere with the CBCT virtual segmentation procedure.\u003c/p\u003e \u003cp\u003eAll extractions were conducted as minimally traumatic as possible to avoid any damage to the tooth or adjacent anatomical structures. Following extractions, teeth were labeled and gently cleansed with water to remove any attached soft tissue and subsequently immersed for 30 min in a 1:10 solution of bleach for decontamination.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Sample preparation\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1. CBCT acquisition and segmentation\u003c/h2\u003e \u003cp\u003ePreoperative CBCT scans were obtained at Harvard School of Dental Medicine using the Veraview X800 MORITA device (MORITA Inc, Kyoto, Japan) for orthodontic, surgical or implant planning diagnosis. Standardized settings were utilized for all CBCT scans, with an 80x40 field of view (FOV), 100 kV, 7 mA, and a resolution of 1.0 mm.\u003c/p\u003e \u003cp\u003eManual segmentations were conducted by a single investigator (AN) following a standardized protocol. The CBCT scans were exported to the Blue-Sky Bio software (Blue Sky Bio, LLC, Libertyville, Illinois), and using the \"Advanced Tooth Segmentation\" tool of this software, segmentations were carried out in the area of interest. Teeth were manually outlined layer by layer using the lasso tool, and subsequent refinements were achieved utilizing the brush tool. Fifteen slices, with a minimum density grey values threshold of 900, and the \u0026ldquo;Smooth\u0026rdquo; function were applied to all manual segmentations. Then, the resulting 3D replicas were saved in standard tessellation language (STL) files \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e AI-driven segmentations were carried out using the Diagnocat\u0026reg; software (Diagnocat, San Francisco, California) by a single investigator (I.P.). This software uses a Convolutional Neural Networks (CNN) algorithm following a progressive coarse-to-fine framework for resolution analysis. Then, the resulting 3D replicas were exported from Diagnocat\u0026reg; to STL files.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 3D printing\u003c/h2\u003e \u003cp\u003eAll 3D replicas were printed with a 3D printer (Formlabs Form 3B+, Formlabs, Somerville, Massachusetts), using Low Force Stereolithography (LFS) technology. Temporary crown-bridge (CB) resin (Formlabs, Somerville, Massachusetts) was used as the printing material. The pre-processed replicas underwent a thorough washing procedure in isopropyl alcohol for 3 minutes following manufacturer recommendations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Post-processing\u003c/h2\u003e \u003cp\u003eFollowing the manufacturer\u0026rsquo;s recommendation, replicas with the supports still attached were first cured in the Form Cure (Formlabs, Somerville, Massachusetts) at 60\u0026deg;C (140\u0026deg;F) for 20 minutes. After the first curing, supports and rafts (3\u0026ndash;5 supports, 0.70mm diameter, only on the occlusal surface) were manually removed, and replicas were carefully sandblasted to refine their surface quality. Finally, the replicas underwent a second curing process at 60\u0026deg;C (140\u0026deg;F) for 20 minutes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4. Surface scanning\u003c/h2\u003e \u003cp\u003eThe extracted teeth and their corresponding 3D pre- and post-processed replicas were digitally scanned using a laboratory scanner (3Shape E Series Lab Scanner, 3Shape, Copenhagen, Denmark). The teeth were secured on the scanner's platform by the root, and the coronal portion of each tooth was scanned first. Next, the teeth were inverted to scan the apical portion. The coronal and apical scans were then superimposed and aligned to create a complete 3D surface model of each tooth, which was then saved as an STL file.\u003c/p\u003e \u003cp\u003eAn overview of the methodology can be found in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Outcome measurements\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1. Volumetric and linear assessment\u003c/h2\u003e \u003cp\u003eFor the volumetric measurements, STL files were analyzed by a single examiner (E.C.Q) using a previously published methodology with a specialized software package (Geomagic Control X, 3D Systems, Rock Hill, SC, USA) [21]. The entire projected volume was measured in mm3 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e The examiner was trained and calibrated by conducting a series of 10 separate volumetric assessments in duplicate.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo assess linear differences, STL files were exported to a software package (Autodesk Meshmixer, San Francisco, California), superimposed, aligned, and compared with the STL files from the extracted tooth. These measurements along the X, Y, and Z axes were carried out by a single examiner (A.N.) using the software's \"Unit/Dimensions\" analysis tool \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Statistical analysis\u003c/h2\u003e \u003cp\u003eSample size was calculated based on data from the study of Lee et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e [19], forecasting a mean difference of 0.38mm in linear measurements using 3-D printed resin replicas (Formlabs\u0026reg;) [18] with a standard deviation of \u0026plusmn;\u0026thinsp;0.22 for the mean linear measurement. Based on an alpha error of 5%, a power of 85%, and a two-sided (equivalence) test, 12 specimens were deemed necessary in this study.\u003c/p\u003e \u003cp\u003eStatistical analyses were done using each extracted tooth as the statistical unit. Outcome variables are presented through descriptive statistics, expressing continuous variables as means, standard deviations (SD), and confidence intervals of 95%, while categorical variables are expressed as percentages (%). Data normality was calculated using a Shapiro-Wilk test.\u003c/p\u003e \u003cp\u003eThe primary outcome variable was the volumetric and linear changes between the final post-processed 3-D printed replicas with the original extracted tooth, with either segmentation method used (i.e., manual or AI segmentation). Differences were evaluated using the 2-sided paired sample Student\u0026rsquo;s T-test, with a p-value of p\u0026thinsp;\u0026le;\u0026thinsp;0.05 as statistically significant. When data did not meet normality criteria, a Wilcoxon signed-rank test was used. Binary categorical data were evaluated with a Chi-squared test. Intraclass correlation coefficients (ICCs) were also calculated for each of these comparisons to evaluate the correlation between the volumetric measurements of different protocols.\u003c/p\u003e \u003cp\u003eSecondary outcomes include time efficiency between different segmentation methods, as well as volumetric and linear changes due to post-processing 3D replicas. Continuous variables used paired Student t-test or Wilcoxon signed-rank test depending on the normality of data, as well as ICCs. All data analyses were performed with SPSS version 21.0 software (Chicago, IL, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Sample Characteristics\u003c/h2\u003e\n \u003cp\u003eThe final sample consisted of 30 extracted teeth from 8 patients (5 males and 3 females) with ages ranging between 13 to 55 years (mean age 32.25, SD 14.69). The extracted teeth comprised multi-rooted teeth (1 mandibular and 3 maxillary molars), and 26 were single-rooted teeth (15 premolars (7 mandibular and 8 maxillary), 4 canines (2 mandibular and 2 maxillary), 5 lateral incisors (2 mandibular and 3 maxillary), and two central incisors (1 mandibular and 1 maxillary)). (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSample characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal (Patients)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e8 (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (62.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026nbsp;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u0026ndash;55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (Teeth)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMolar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePremolar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCanine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLateral Incisor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Incisor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Roots\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMulti (Three)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMulti (Two)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26 (86.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Intra-examiner Reliability\u003c/h2\u003e\n \u003cp\u003eThe calibration exercise provided a high intra-examiner agreement, with a strong ICC ranging from 0.97\u0026ndash;0.99 for the volumetric and linear analysis (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eIntraclass correlation analysis of volumetric and linear measurements.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVolumetric\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLinear (Y)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eICC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eICC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePair 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExtracted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVs. Scan_Manual_Postprocessed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePair 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExtracted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVs. Scan_AI_Postprocessed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePair 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExtracted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVs. Segmentation_Manual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePair 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExtracted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVs. Segmentation_AI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePair 5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSegmentation_AI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVs. Segmentation_Manual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePair 6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePreprocessed_pooled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVs. Postprocessed_pooled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparative analysis of volumetric and linear measurements of STL files from virtual segmentation replicas\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"12\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(A)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVolumetric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear (Y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePair 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExtracted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-1.62, 12.92]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.34, 0.59]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVs. Scan_Manual_Postprocessed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePair 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExtracted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[16.56, 27.70]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0. 53, 0.89]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVs. Scan_AI_Postprocessed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePair 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExtracted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-5.84, 9.37]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.12, 0.33]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVs. Segmentation_Manual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePair 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExtracted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[7.99, 16.46]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0. 34, 0.76]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVs. Segmentation_AI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePair 5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSegmentation_AI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[3.99, 16.95]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0. 12, .054]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVs. Segmentation_Manual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(B)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVolumetric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear (Y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePair 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScan_AI_Preprocessed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[18.98, 23.85]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-.008, 0.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVs. Scan_AI_Postprocessed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePair 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScan_Manual_Preprocessed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[19.27, 24.68]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.02, 0.16]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVs. Scan_Manual_Postprocessed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePair 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePreprocessed_pooled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[19.93, 23.46]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.01, 0.30]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVs. Postprocessed_pooled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003e\u003cstrong\u003e(A)\u003c/strong\u003e AI-driven and manually driven analysis.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003e\u003cstrong\u003e(B)\u003c/strong\u003e Post-processed effect analysis\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003e3.3. Accuracy of the comprehensive Computer-Aided Rapid Prototyping (CARP) process using either manual segmentation or AI-driven segmentation\u003c/strong\u003e\u003c/p\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.1 Linear measurements\u003c/h2\u003e\n \u003cp\u003eA mean linear difference of 0.463mm (SD 0.335) was observed when comparing post-processed 3-D replicas obtained by manual segmentation with the corresponding extracted teeth. These differences were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e\n \u003cp\u003eSimilarly, the mean linear difference when comparing post-processed 3-D replicas obtained by AI segmentation with the corresponding extracted teeth was 0.709mm (SD 0.491), with these differences being statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e\n \u003cp\u003eDirect comparison between the 3D replicas obtained from manual and AI segmentation resulted in a mean linear difference of 0.221mm (SD 0.281). These differences were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.2. Volumetric measurements\u003c/h2\u003e\n \u003cp\u003eA mean volumetric difference of 5.651mm\u003csup\u003e3\u003c/sup\u003e (SD 19.469) was obtained between the manually segmented 3D-printed replicas and the extracted teeth, corresponding to a 1.20% volume reduction. These differences, however, were not statistically significant (p\u0026thinsp;=\u0026thinsp;0.123) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA and Table\u0026nbsp;4).\u003c/p\u003e\n \u003cp\u003eConversely, the mean volumetric difference when comparing replicas obtained by AI segmentation with the corresponding extracted teeth was 22.128mm\u0026sup3; (SD 14.917), corresponding to a -4.70% volume reduction. These differences were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA and Table\u0026nbsp;4).\u003c/p\u003e\n \u003cp\u003eDirect comparison between STL files (3D surfaces) generated from AI-driven and manual segmentation found an overall mean volumetric difference of 10.466mm\u0026sup3; (SD 17.354, p\u0026thinsp;=\u0026thinsp;0.003), equivalent to a -2.23% change in volume (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA and Table\u0026nbsp;4). Replicas from AI-driven segmentation were smaller than those originating from manual segmentation\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable.4. Descriptive volumetric comparison.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.467492260061919%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.95046439628483%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003eVolumetric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.467492260061919%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.95046439628483%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean diff\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e% diff in Vol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.467492260061919%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePair 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.95046439628483%\" valign=\"top\"\u003e\n \u003cp\u003eExtracted tooth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e470.782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e5.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e-1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.467492260061919%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.95046439628483%\" valign=\"top\"\u003e\n \u003cp\u003eVs. Scan_Manual_Postprocessed 3D printed replica\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e465.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.467492260061919%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePair 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.95046439628483%\" valign=\"top\"\u003e\n \u003cp\u003eExtracted tooth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e470.782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e22.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e-4.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.467492260061919%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.95046439628483%\" valign=\"top\"\u003e\n \u003cp\u003eVs. Scan_AI_Postprocessed 3D \u0026nbsp; printed replica\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e448.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.467492260061919%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePair 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.95046439628483%\" valign=\"top\"\u003e\n \u003cp\u003eExtracted tooth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e470.782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e1.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e-0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.467492260061919%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.95046439628483%\" valign=\"top\"\u003e\n \u003cp\u003eVs. Segmentation_Manual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e469.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.467492260061919%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePair 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.95046439628483%\" valign=\"top\"\u003e\n \u003cp\u003eExtracted tooth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e470.782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e12.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e-2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.467492260061919%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.95046439628483%\" valign=\"top\"\u003e\n \u003cp\u003eVs. Segmentation_AI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e458.550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.467492260061919%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePair 5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.95046439628483%\" valign=\"top\"\u003e\n \u003cp\u003eSegmentation_AI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e458.550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e10.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e-2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.467492260061919%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.95046439628483%\" valign=\"top\"\u003e\n \u003cp\u003eVs. Segmentation_Manual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e469.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.467492260061919%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.95046439628483%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.467492260061919%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.95046439628483%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003eVolumetric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.467492260061919%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.95046439628483%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean diff\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e% diff in Vol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.467492260061919%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePair 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.95046439628483%\" valign=\"top\"\u003e\n \u003cp\u003eScan_AI_Preprocessed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e470.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e21.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e-4.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.467492260061919%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.95046439628483%\" valign=\"top\"\u003e\n \u003cp\u003eVs. Scan_AI_Postprocessed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e448.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.467492260061919%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePair 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.95046439628483%\" valign=\"top\"\u003e\n \u003cp\u003eScan_Manual_Preprocessed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e487.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e21.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e-4.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.467492260061919%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.95046439628483%\" valign=\"top\"\u003e\n \u003cp\u003eVs. Scan_Manual_Postprocessed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e465.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.467492260061919%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePair 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.95046439628483%\" valign=\"top\"\u003e\n \u003cp\u003ePreprocessed_pooled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e478.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e21.697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e-4.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.467492260061919%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.95046439628483%\" valign=\"top\"\u003e\n \u003cp\u003eVs. Postprocessed_pooled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e456.893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.860681114551083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003cp\u003eComparisons for pairs 1, 2, 4, and 5 were conducted by establishing the extracted teeth as a reference for comparing the size of the replicas. In the case of pair 3, AI-segmented 3D replicas were compared against manually segmented replicas, with the manual group serving as the reference. For all post-processing groups, pre-processed replicas were used as the reference for comparison.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e** \u0026ldquo;Segmentation manual\u0026rdquo; and \u0026ldquo;Segmentation IA\u0026rdquo; are digital files (STL) that have yet to be printed.\u003c/p\u003e\n \u003cp\u003e** \u0026ldquo;Pre-processed\u0026rdquo; and \u0026ldquo;Post-processed\u0026rdquo; represent digital files (STL) obtained after scanning the 3D-printed replicas.\u003c/p\u003e\n \u003ch2\u003e3.4. Accuracy of post-processed replicas in comparison to preprocessed replicas\u003c/h2\u003e\n \u003cp\u003eComparative analyses were conducted by separately comparing all the teeth replicas generated through AI-driven segmentation and manual segmentation, pre- and post-processing. Subsequently, data from all replicas were pooled together to assess the overall impact of post-processing on both volumetric and linear measurements.\u003c/p\u003e\n \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\n \u003ch2\u003e3.4.1. Linear measurements\u003c/h2\u003e\n \u003cp\u003eAnalyzing the pre- and post-processed replicas from AI-driven segmentation showed a mean linear difference of 0.210mm (SD 0.777), demonstrating no statistical significance (p\u0026thinsp;=\u0026thinsp;0.15). In contrast, the same comparison for the manual segmentation counterpart group indicated a statistically significant mean linear difference of 0.093mm (SD 0.190, p\u0026thinsp;=\u0026thinsp;0.012). Combining both groups, an overall mean linear difference of 0.151mm (SD 0.564) was observed, and this difference was statistically significant (p\u0026thinsp;=\u0026thinsp;0.042) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\n \u003ch2\u003e3.4.2. Volumetric measurements\u003c/h2\u003e\n \u003cp\u003eWhen comparing the pre and post-processed replicas obtained from AI-driven segmentation, there was a mean volumetric difference of 21.419mm\u0026sup3; (SD 6.523),-4.56% volume reduction. The same comparison for the replicas obtained from manual segmentation showed a mean volumetric difference of 21.975mm\u0026sup3; (SD 7.232), -4.51% volume reduction. When both groups (AI and manually segmented) were pooled together, an overall mean volumetric difference of 21.697mm\u0026sup3; (SD 6.833), -4.53% volume reduction, was found between pre-processed and post-processed replicas (Table.4). Importantly, all groups demonstrated statistically significant results (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table.3B). Post-processed replicas were generally smaller than the pre-processed ones.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5. Comparison of segmentation times between manual and AI-driven methods\u003c/h2\u003e\n \u003cp\u003eThe average time required for manual segmentation of 30 teeth was 23.97 minutes, and the average time required for AI-driven segmentation of the same teeth was reported to be 2.1 minutes.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eThe application of CARP in tooth autotransplantation involves multiple steps, which include CBCT acquisition and segmentation, 3D printing, and subsequent post-processing of 3D-printed replicas. The accuracy of each step may have a substantial impact on the overall accuracy of the final 3D replica. This study validates the entirety of the CARP process, assessing the accuracy of manual and AI tooth segmentation and its resulting 3D-printed replicas compared with the reference extracted teeth. We observed that both methods were reliable and suitable for the fabrication of 3D tooth replicas, as the observed statistically significant differences between methods can be considered non-clinically significant. However, time-efficiency analysis demonstrates a reduction in time of 21 minutes for the AI-driven method.\u003c/p\u003e \u003cp\u003eManual segmentation is a well-established method to obtain tooth replicas, and previous studies have validated its accuracy, considering it to be the gold standard [2]. However, it is a time-consuming process that demands training and experience and relies on the interpretation skills of the operator. The average time for manual segmentation of each tooth in this investigation was 23.97 minutes. Nonetheless, other studies, such as Lee et al. [22], reported an average time of 15 minutes per tooth. Another study on manual segmentations of single and double-rooted teeth reported an average time of 6.6 minutes. Interestingly, AI-driven segmentation resulted in a 12.5-fold reduced time compared to manual segmentation [23]. Considering that manual segmentation involves the investigator selecting and individually outlining multiple image slices, the reported time in different studies can vary significantly based on the type of teeth, the number of slices selected, and the precision of the outlining process [1, 22, 24]. This fact was noted in this study considering the higher standard deviations in the manual vs. the AI method.\u003c/p\u003e \u003cp\u003eSeveral AI algorithms and deep learning models have recently been developed to carry out a fully automatic tooth segmentation more efficiently within a few minutes [1, 23, 25\u0026ndash;27]. One of the most effective models is the CNN, which has been integrated into the software used for AI-driven segmentation in this study [1, 28]. Comprising multilayer neural networks, CNN algorithms excel in identifying visual patterns quickly and with minimal pre-processing requirements [1, 28]. However, these models have certain limitations, and recent review studies have underscored the necessity for validating their accuracy and reliability [1]. Several challenges noted in other studies involve the segmentation of intricate root anatomy and apices, supernumerary and impacted teeth, especially third molars, and cases of crowding [1, 17, 23, 25, 29]. These factors may reasonably account for our findings regarding the lower accuracy of AI-driven segmentation versus the manual segmentation group. This finding may also be explained by the fact that manual segmentation was performed by the same experienced operator under ideal and controlled circumstances.\u003c/p\u003e \u003cp\u003eThis study demonstrated a reduction in volume (-0.38 to -2.6% for manual and AI segmentation, respectively) when comparing the virtual files obtained after segmentation and the scanned tooth. Interestingly, volumetric and linear analyses of post-processed replicas showed a smaller trend compared to the extracted teeth (-4.53% volume reduction). These findings could be attributed to resin shrinkage during post-curing. Similarly, a study by Lee and Kim also reported that 3D replicas from CT images were generally smaller than the actual teeth [30]. Their results revealed that, on average, the 3D images of donor teeth were \u0026minus;\u0026thinsp;0.149 mm smaller than the actual teeth, and the 3D replicas were, on average, -0.067 mm smaller than their corresponding 3D images. Despite the observed size discrepancy, it is noteworthy that this error may be clinically acceptable for the application of these replicas in the context of tooth autotransplantation therapy. Recognizing the benefits of utilizing 3D replicas to reduce extraoral time and minimize damage to the periodontal ligament, the size discrepancy can be clinically manageable [7]. This factor, coupled together with the volume reduction after virtual segmentation reported in this study, can be taken into consideration during the planning phase. Therefore, clinicians should be aware that the surgical area should be minimally overprepared based on the 3D replica and to allow some physical space for blood clot formation and establishment around the roots of the autotransplanted tooth.\u003c/p\u003e \u003cp\u003eAnother important aspect to consider is that, while this study reports AI-driven segmentation as less accurate than the manual method, it was notably more time-efficient and less dependent on operator input, demonstrated by a higher SD on the manual-segmentation group. Continuous advancements in AI algorithms and deep learning models hold the promise of significant improvements in tooth segmentation software. By harnessing the potential for ongoing training and improvement of AI systems, there is a clear path towards achieving higher accuracy and efficiency in digital segmentation processes. Efficiency is a significant factor in treatment planning and the practice of modern dentistry. In the context of autotransplantation procedures, earlier studies indicated that using 3D replicas can significantly enhance the success and efficiency of surgery. Shahbazian et al. and Verweij et al., reported extra-oral times of less than 1 minute when 3D replicas were employed and an overall significant reduction in procedural time [7, 8, 31, 32]. If AI can streamline the treatment planning phase by reducing the time and effort required for tooth segmentation while maintaining an acceptable level of accuracy, it holds the potential to be a promising tool for enhancing the overall efficiency of surgical treatment planning.\u003c/p\u003e \u003cp\u003eThe relevance of this investigation lies in a direct evaluation of the accuracy of AI-driven tooth segmentation, both in terms of volumetric and linear data. However, this study also presents some limitations that should be acknowledged. Firstly, the strict inclusion criteria and consistent use of the same CBCT machine, parameters, and standardized operator enhance the study's internal validity but also make it challenging to extrapolate these findings to other protocols. Secondly, only one software for AI-driven segmentation has been tested, as well as 3D printing workflow, and the reported accuracy may not apply to other approaches utilizing different technologies. Lastly, despite conducting a sample size calculation and achieving high power, further studies with larger sample sizes are recommended to investigate the influence of multi-radicular teeth, furcation areas, and complex anatomy on the segmentation process, as well as the relationship between the time dedicated to manual segmentation and its final accuracy.\u003c/p\u003e"},{"header":"5. CONCLUSIONS","content":"\u003cp\u003eWithin the limitations of this study, the following conclusions can be inferred:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eAI-driven and manual virtual tooth segmentation methods are reliable and suitable for obtaining 3D tooth replicas.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAI-driven tooth segmentation proved to be more time-efficient and independent of the operator's experience. \u0026nbsp;\u003c/li\u003e\n \u003cli\u003ePost-processing 3D-printed tooth replicas showed consistently reduced dimensions.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors’ contributions:\u003c/strong\u003e IP (Concept/Design, Data analysis/interpretation, Critical revision of article, Data collection, Approval of article); I.P, A.N, J.C, E.C.Q, M.S (Data analysis/interpretation, Critical revision of article, Data collection, Writing, Approval of article), W.V.G, G.G (Critical revision of article, Approval of article). All authors critically revised the manuscript, gave final approval, and agreed to be accountable for all aspects of the scientific work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e The authors have no conflicts of interest to report pertaining to the conduction of this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u003c/strong\u003e The data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval statement:\u003c/strong\u003e This study was approved by the Harvard of Dental Medicine Institutional Review Board (IRB21-1687).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement:\u003c/strong\u003e No financial support or sponsorship was received for the conduction of this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNo datasets were generated or analysed during the current study.\u003c/p\u003e\n\u003cp\u003eNo funding has been obtained for the development of this investigation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003ePolizzi A, Quinzi V, Ronsivalle V, Venezia P, Santonocito S, Lo Giudice A, Leonardi R and Isola G (2023) Tooth automatic segmentation from CBCT images: a systematic review. 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IEEE,\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHao J, Liao W, Zhang YL, Peng J, Zhao Z, Chen Z, Zhou BW, Feng Y, Fang B, Liu ZZ and Zhao ZH (2022) Toward Clinically Applicable 3-Dimensional Tooth Segmentation via Deep Learning. Journal of dental research 101:304-311. doi: 10.1177/00220345211040459\u003c/li\u003e\n \u003cli\u003eLee S-J and Kim E-S (2012) Minimizing the extra-oral time in autogeneous tooth transplantation: use of computer-aided rapid prototyping (CARP) as a duplicate model tooth. Restorative dentistry \u0026amp; endodontics 37:136-141.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eVerweij JP, Moin DA, Mensink G, Nijkamp P, Wismeijer D and Merkesteyn JPRv (2016) Autotransplantation of Premolars With a 3-Dimensional Printed Titanium Replica of the Donor Tooth Functioning as a Surgical Guide: Proof of Concept. 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ORAL SURGERY ORAL MEDICINE ORAL PATHOLOGY ORAL RADIOLOGY 115:667-675. doi: 10.1016/j.oooo.2013.01.025\u003c/li\u003e\n\u003c/ol\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":"Autotransplantation, Computer Aided Manufacturing, Digital Dentistry Artificial Intelligence, Three-dimensional Printing, Stereolithography.","lastPublishedDoi":"10.21203/rs.3.rs-4576625/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4576625/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives: \u003c/strong\u003eThe primary aim of this investigation was to validate a method for generating 3D replicas through virtual segmentation, utilizing artificial intelligence (AI) or manual-driven methods, assessing accuracy in terms of volumetric and linear discrepancies. The secondary aims were the assessment of time efficiency with both segmentation methods and the effect of post-processing 3D replicas.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThirty teeth were scanned through Cone Beam Computed Tomography (CBCT), capturing the region of interest from human subjects. DICOM files underwent segmentation through both AI and manual-driven methods. Replicas were fabricated with a stereolithography 3D printer. After surface scanning of pre-processed replicas and extracted teeth, STL files were superimposed to evaluate linear and volumetric differences using the extracted teeth as the reference. Post-processed replicas were scanned to assess the effect of post-processing on linear and volumetric changes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e AI-driven segmentation resulted in statistically significant mean linear and volumetric differences of -0.709mm and -4.70%, respectively. Manual segmentation showed no statistically significant differences in mean linear (-0.463mm) and volumetric (-1.20%) measures. Comparing manual and AI-driven segmentations, showed that AI-driven segmentation displayed mean linear and volumetric differences of -0.329mm and -2.23%, respectively. Additionally, AI segmentation reduced mean time by 21.8 minutes. When comparing post-processed to pre-processed replicas, there was a volumetric reduction of -4.53% and a mean linear difference of -0.151mm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Both segmentation methods achieved acceptable accuracy, with manual segmentation slightly more accurate and AI-driven segmentation more time-efficient. Continuous improvement in AI offers the potential for increased accuracy, efficiency, and broader application in the future.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Significance: \u003c/strong\u003eTooth replica generation in the context of tooth autotransplantation therapy may contribute to enhanced success and survival rates. Accurate CBCT-based virtual segmentation and 3D printing technologies are particularly important in the fabrication of 3D replicas. Therefore, it is crucial to assess the accuracy of available techniques and alternatives to demonstrate their reliability and accuracy in the fabrication of tooth replicas.\u003c/p\u003e","manuscriptTitle":"Generation of Tooth Replicas by Virtual Segmentation Using Artificial Intelligence","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-11 10:40:43","doi":"10.21203/rs.3.rs-4576625/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":"f6187af4-8c8d-44dc-b858-2613f81ea54d","owner":[],"postedDate":"July 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-18T13:51:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-11 10:40:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4576625","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4576625","identity":"rs-4576625","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}