RESUMO
The rising demand for dental implants necessitates addressing anatomical challenges, particularly the shape of the mandible. Incorrectly angling implants can cause lingual perforations, risking damage to the inferior alveolar artery and nerve. This study analyzed 96 cone-beam computed tomography images from individuals aged 20 to 70 (8 males and 8 females) to evaluate mandibular anatomy in four areas: left and right sides and the first and second molars. Mandibular shapes were classified into U, C, and P types. U-shaped mandibles, with a wider crest width, pose the highest risk of lingual perforation. Measurements for U-shaped types included concavity angle, length, and depth. Statistical analyses (T-tests and ANOVA) with a 95% confidence interval showed no significant differences between the left and right sides. However, significant differences based on gender, age, and tooth type were found. The study found U-shapes in 34.6% of cases, P-shapes in 28.9%, and C-shapes in 36.5%, with U-shapes more common in second molars. Understanding these variations enhances the safety and effectiveness of implant procedures and oral surgeries.
RESUMO
With the growing demand for orthognathic surgery and other facial treatments, the accurate identification of anatomical landmarks has become crucial. Recent advancements have shifted towards using three-dimensional radiologic analysis instead of traditional two-dimensional methods, as it allows for more precise treatment planning, primarily relying on direct identification by clinicians. However, manual tracing can be time-consuming, mainly when dealing with a large number of patients. This study compared the accuracy and reliability of identifying anatomical landmarks using artificial intelligence (AI) and manual identification. Thirty patients over 19 years old who underwent pre-orthodontic and orthognathic surgery treatment and had pre-orthodontic three-dimensional radiologic scans were selected. Thirteen anatomical indicators were identified using both AI and manual methods. The landmarks were identified by AI and four experienced clinicians, and multiple ANOVA was performed to analyze the results. The study results revealed minimal significant differences between AI and manual tracing, with a maximum deviation of less than 2.83 mm. This indicates that utilizing AI to identify anatomical landmarks can be a reliable method in planning orthognathic surgery. Our findings suggest that using AI for anatomical landmark identification can enhance treatment accuracy and reliability, ultimately benefiting clinicians and patients.
RESUMO
BACKGROUND: Multi-dimensional facial imaging is increasingly used in hospital clinics. A digital twin of the face can be created by reconstructing three-dimensional (3D) facial images using facial scanners. Therefore, the reliability, strengths, and weaknesses of scanners should be investigated and approved; Methods: Images obtained from three facial scanners (RayFace, MegaGen, and Artec Eva) were compared with cone-beam computed tomography images as the standard. Surface discrepancies were measured and analyzed at 14 specific reference points; Results: All scanners used in this study achieved acceptable results, although only scanner 3 obtained preferable results. Each scanner exhibited weak and strong points because of differences in the scanning methods. Scanner 2 exhibited the best result on the left endocanthion; scanner 1 achieved the best result on the left exocanthion and left alare; and scanner 3 achieved the best result on the left exocanthion (both cheeks); Conclusions: These comparative analysis data can be used when creating digital twins through segmentation, selecting and merging data, or developing a new scanner to overcome all shortcomings.