Deep learning for categorization of endodontic lesion based on radiographic periapical index scoring system.

OBJECTIVE: The study aimed to apply convolutional neural network (CNN) to score periapical lesion on an intraoral periapical radiograph (IOPAR) based on the periapical index (PAI) scoring system. MATERIALS AND METHODS: A total of 3000 periapical root areas (PRA) on 1950 digital IOPAR were pre-scored by three endodontists. This data was used to train the CNN model-"YOLO version 3." A total of 450 PRA was used for validation of the model. Data augmentation techniques and model optimization were applied. A total of 540 PRA on 250 digital IOPAR was used to test the performance of the CNN model. RESULTS: A total of 303 PRA (56.11%) exhibited true prediction. PAI score 1 showed the highest true prediction (90.9%). PAI scores 2 and 5 exhibited the least true prediction (30% each). PAI scores 3 and 4 had a true prediction of 60% and 71%, respectively. When the scores were dichotomized as healthy (PAI scores 1 and 2) and diseased (PAI score 3, 4, and 5), the model achieved a true prediction of 76.6% and 92%, respectively. The model exhibited a 92.1% sensitivity/recall, 76% specificity, 86.4% positive predictive value/precision, and 86.1% negative predictive value. The accuracy, F1 score, and Matthews correlation coefficient were 86.3%, 0.89, and 0.71, respectively. CONCLUSION: The CNN model trained on a limited amount of IOPAR data showed potential for PAI scoring of the periapical lesion on digital IOPAR. CLINICAL RELEVANCE: An automated system for PAI scoring is developed that would potentially benefit clinician and researchers.

Association between the Mesiobuccal Canal Configuration, Interorifice Distance, and the Corresponding Root Length of Permanent Maxillary First Molar Tooth: A Cone-beam Computed Tomographic Study.

INTRODUCTION: The aim of this cone-beam computed tomographic study was to evaluate the association between the mesiobuccal root canal configuration (RCC), interorifice distance (IOD), and the corresponding root length of a permanent maxillary first molar tooth. METHODS: One hundred cone-beam computed tomographic scans obtained from the computerized data bank of the institute were studied. The IOD between the first mesiobuccal and second mesiobuccal canal was measured in the axial section where the second mesiobuccal canal was first visualized. The root length was measured from the cementoenamel junction to the root apex in the coronal and sagittal section. The associations of these parameters with the RCC (based on Vertucci's classification) were evaluated. RESULTS: The predominant RCC was observed to be Vertucci type II (89%). The mean root length with this configuration was 11.19 ± 1.35 mm. In type IV RCC, the mean root length was 9.13 ± 0.52 mm. A statistically significant association was established between the root length and RCC (P < .05). In roots with type II and type IV RCC, the mean IOD was 2.58 ± 0.04 mm and 2.62 ± 0.1 mm, respectively. No statistically significant relation was established between the IOD and the type of RCC (P > .05). CONCLUSIONS: The length of the mesiobuccal root is an important anatomic parameter for predicting the type of RCC in the permanent maxillary first molar tooth.