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.
(© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)