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INTRODUCTION: The accuracy of tooth segmentation in intraoral scans is crucial for performing virtual setups and appliance fabrication. Hence, the objective of this study was to estimate and compare the accuracy of automated tooth segmentation generated by the artificial intelligence of dentOne software (DIORCO Co, Ltd, Yongin, South Korea) and Medit Ortho Simulation software (Medit Corp, Seoul, South Korea). METHODS: Twelve maxillary and mandibular pretreatment dental scan sets comprising 286 teeth were collected for this investigation from the archives of the Department of Orthodontics, Faculty of Dentistry, Alexandria University. The scans were imported as standard tessellation language files into both dentOne and Medit Ortho Simulation software. Automatic segmentation was run on each software. The number of successfully segmented teeth vs failed segmentations was recorded to determine the success rate of automated segmentation of each program. Evaluation of success and/or failure was based on the software's identification of the teeth and the quality of the segmentation. The mesiodistal tooth width measurements after segmentation using both tested software programs were compared with those measured on the unsegmented scan using Meshmixer software (Autodesk, San Rafael, Calif). The unsegmented scans served as the reference standard. RESULTS: A total of 288 teeth were examined. Successful identification rates were 99% and 98.3% for Medit and dentOne, respectively. Success rates of segmenting the lingual surfaces of incisors were significantly higher in Medit than in dentOne (93.7% vs 66.7%, respectively; P <0.001). DentOne overestimated the mesiodistal width of canines (0.11 mm, P = 0.032), premolars (0.22 mm, P < 0.001), and molars (0.14 mm, P = 0.043) compared with the reference standard, whereas Medit overestimated the mesiodistal width of premolars only (0.13 mm, P = 0.006). Bland-Altman plots showed that mesiodistal tooth width agreement limits exceeded 0.2 mm between each software and the reference standard. CONCLUSIONS: Both artificial intelligence-segmentation software demonstrated acceptable accuracy in tooth segmentation. There is a need for improvement in segmenting incisor lingual tooth surfaces in dentOne. Both software programs tended to overestimate the mesiodistal widths of segmented teeth, particularly the premolars. Artificial intelligence-segmentation needs to be manually adjusted by the operator to ensure accuracy. However, this still does not solve the problem of proximal surface reconstruction by the software.
RESUMO
BACKGROUND: The aim of the study was to evaluate the cephalometric and dentoalveolar characteristics of maxillary lateral incisor agenesis patients, and to compare the findings to a matched control group without tooth agenesis, excluding third molars, from the same population. METHODS: The pre-orthodontic records of 72 non-growing patients, who were treated at the Orthodontic Department, Faculty of Dentistry, Alexandria University, were used to address the aim of this retrospective study. Patients having unilateral or bilateral maxillary lateral incisor agenesis, with no history of previous orthodontic treatment, congenital craniofacial malformations, facial trauma, or surgeries were divided into two test groups based on the pattern of maxillary lateral incisors agenesis (group I: unilateral (UMLIA), group II: bilateral (BMLIA)). A control group (group III (CTRL)) having a complete set of permanent dentition (excluding third molars), and having no dental anomalies was age-matched with the test groups. Measurements were performed on the pre-orthodontic lateral cephalometric radiographs and the pre-orthodontic digital dental casts. The measured variables were compared between the groups using one-way ANOVA and Kruskal Wallis tests according to the normality of the variable. In case of significant results, both tests were followed by multiple pairwise comparisons using Bonferroni adjusted significance level. Significance level was set at P < 0.05. RESULTS: BMLIA group showed a smaller SNA angle and maxillary length, a more negative ANB angle and Wits appraisal, and a larger Maxillo-mandibular differential than UMLIA and/or CTRL group. The dental and soft tissue cephalometric measurements did not show any significant differences between the groups. Dentoalveolar cast measurements showed that BMLIA patients presented with significantly smaller maxillary inter-canine width than UMLIA and CTRL patients. CONCLUSIONS: Cephalometric analysis has shown that subjects with BMLIA have a statistically significant reduced ANB and maxillary length. Tooth eruption may play a role in the development of the maxillary arch.
