On using convolutional neural networks to classify periodontal bone destruction in periapical radiographs

Periodontitis is an oral disease that promotes not only inflammation but also tissue d estruction, which is visible in periapical radiographs. Early diagnosis is essential to prevent the progression of this lesion. This work's main objective is to classify regions in periapical examinations according to the presence of periodontal bone destruction. This study considered 1079 interproximal regions extracted from 467 periapical radiographs. This data was annotated by experts and used to train a ResNet and an Inception model, which were after evaluated with a test set. Inception presented the best results and an impressive rate of correctness even on the small and unbalanced dataset. The final accuracy, precision, recall, specificity, and negative predictive values are 0.817, 0.762, 0.923, 0.711, and 0.902, respectively. The ROC and PR curves also demonstrate the good performance of both models. These results suggest that the evaluated CNN model can be used as a clinical decision support tool to diagnose periodontal bone destruction in periapical exams.