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Background: This retrospective study aims to characterise the root canal morphology of maxillary and mandibular second molars using cone-beam computed tomography (CBCT). The number of roots and canal configurations were evaluated using both the Vertucci and Benjamı´n Brisen˜ o Marroquı´n classification systems. Methods: A total of 1084 second molar images (523 maxillary; 266 right and 257 left side and 561 mandibular; 285 right and 276 left side) were evaluated from 320 CBCT scans analyzed for the Turkish subpopulation. CBCT imaging provided superior visualisation of root canal anatomy compared to periapical radiography. The findings revealed diverse root canal configurations, with variations observed even within the same population. Statistical analyses, including the chi-squared test, were used to assess correlations between root number and demographic variables such as age and sex. Results: According to Benjamı´n Brisen˜ o Marroquı´n classification system, the most common configuration for upper right three-rooted teeth mesial root was 3URM2−1 (n:66, 35.7%), for distal root was 3URM1 (n:169, 91.4%), and for palatal root was 3URM1 (n:165, 89.2%). Additionally, the most common configuration for upper left three-rooted teeth mesial root was 3271 (n:50, 28.4%), for distal root was 3ULM1 (n:160, 90.9%), and for palatal root was 3ULM1 (n:158, 89.8%). In lower left molars, the most common configuration in the two-rooted teeth mesial root was 2LLM2 (n:114, 49.4%), and for the distal root was 2LLM1 (n:170, 73.6%). For lower right the most common configuration for two-rooted teeth mesial root was 2LRM2 (n:125, 52.5%), and for distal root was 2LRM1 (n:173, 72.7%)(p < 0.05). Conclusion: The primary outcome was observed that the root canal anatomy of upper and lower second molars may differ in both classifications of Turkish subpopulation. While Vertucci's classification was inadequate in some cases, Briseno-Marroquin classification was able to classify all upper and lower second molars with a single code. This new classification is a more useful system for classifying all second molars. There is a statistically significant difference exists among the new configuration according to the distribution of the teeth analyzed. [ABSTRACT FROM AUTHOR]

Objectives: The introduction of artificial intelligence (AI) about great changes in the field of dentistry, but it has not yet been fully determined in which areas it will make a positive contribution to dentistry students. The objective of our study was to compare the diagnostic accuracy of undergraduate students (fourth-year dentistry students [4DS] and final-year dentistry students [5DS]) and AI when examining panoramic radiographs., Methods: Fifty panoramic radiographs and 1602 teeth were examined by 50 4DS who had not received a clinical practice internship, 50 5DS, and an AI application. The participants and the AI application evaluated the teeth seen in each radiograph one by one in terms of caries, fillings, teeth with root canal treatment, periodontal loss, extractions, crowns, teeth with apical lesions, and impacted and extracted teeth. Findings were recorded in an Excel chart. Chi-square analysis was used to compare diagnostic success between the groups., Results: The results indicate that there was a statistically significant difference in the identified accuracy of caries, fillings, and extractions between the AI application and undergraduate students (p < 0.05). Although AI showed more identified accuracy in teeth with apical lesions, impacted teeth, and teeth with root canal treatment than in undergraduate students, there was no significant difference between them (p > 0.05)., Conclusion: AI exhibited better results than undergraduate students especially in the detection of caries and fillings. AI could improve undergraduates' accuracy in detecting caries, fillings, and extractions and help them make accurate treatment decisions. In cases where dentistry students are examining patients using panoramic radiographs, employing AI programs during their clinical training to confirm and strengthen the student's diagnosis may be a promising new development., (© 2024 American Dental Education Association.)

Background: There is information missing in the literature about the comparison of dentists vs. artificial intelligence (AI) based on diagnostic capability. The aim of this study is to evaluate the diagnostic performance based on radiological diagnoses regarding caries and periapical infection detection by comparing AI software with junior dentists who have 1 or 2 years of experience, based on the valid determinations by specialist dentists., Methods: In the initial stage of the study, 2 specialist dentists evaluated the presence of caries and periapical lesions on 500 digital panoramic radiographs, and the detection time was recorded in seconds. In the second stage, 3 junior dentists and an AI software performed diagnoses on the same panoramic radiographs, and the diagnostic results and durations were recorded in seconds., Results: The AI and the three junior dentists, respectively, detected dental caries at a sensitivity (SEN) of 0.907, 0.889, 0.491, 0.907; a specificity (SPEC) of 0.760, 0.740, 0.454, 0.696; a positive predictive value (PPV) of 0.693, 0.470, 0.155, 0.666; a negative predictive value (NPV) of 0.505, 0.415, 0.275, 0.367 and a F1-score of 0.786, 0.615, 0.236, 0.768. The AI and the three junior dentists respectively detected periapical lesions at an SEN of 0.973, 0.962, 0.758, 0.958; a SPEC of 0.629, 0.421, 0.404, 0.621; a PPV of 0.861, 0.651, 0.312, 0.648; a NPV of 0.689, 0.673, 0.278, 0.546 and an F1-score of 0.914, 0.777, 0.442, 0.773. The AI software gave more accurate results, especially in detecting periapical lesions. On the other hand, in caries detection, the underdiagnosis rate was high for both AI and junior dentists., Conclusions: Regarding the evaluation time needed, AI performed faster, on average., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-23-762/coif). The authors have no conflicts of interest to declare., (2023 Quantitative Imaging in Medicine and Surgery. All rights reserved.)