Deep learning-based detection of irreversible pulpitis in primary molars.

Abstract

Changes in healthy and inflamed pulp on periapical radiographs are traditionally so subtle that they may be imperceptible to human experts, limiting its potential use as an adjunct clinical diagnostic feature. This study aimed to investigate the feasibility of an image-analysis technique based on the convolutional neural network (CNN) to detect irreversible pulpitis in primary molars on periapical radiographs (PRs). This retrospective study was performed in two health centres. Patients who received indirect pulp therapy at Peking University Hospital for Stomatology were retrospectively identified and randomly divided into training and validation sets (8:2). Using PRs as input to an EfficientNet CNN, the model was trained to categorise cases into either the success or failure group and externally tested on patients who presented to our affiliate institution. Model performance was evaluated using sensitivity, specificity, accuracy and F1 score. A total of 348 PRs with deep caries were enrolled from the two centres. The deep learning model achieved the highest accuracy of 0.90 (95% confidence interval: 0.79-0.96) in the internal validation set, with an overall accuracy of 0.85 in the external test set. The mean greyscale value was higher in the failure group than in the success group (p = .013). The deep learning-based model could detect irreversible pulpitis in primary molars with deep caries on PRs. Moreover, this study provides a convenient and complementary method for assessing pulp status.