Deep learning based dental implant failure prediction from periapical and panoramic films

Background: Dental implant failure is a critical condition that can seriously compromise therapeutic efficacy. Insufficient bone volume, unfavorable bone quality and periodontal bone loss in radiographic films, as well as systemic condition such as osteopenia/osteoporosis and diabetes mellitus, have been associated with implant failure. Early alerts for potential implant failure could effectively mitigate the risk of severe symptoms. This study aimed to develop an effective implant outcome prediction model using dental periapical and panoramic films.Methods: A total of 248 patients (89 with failed implants and 159 with successful implants) were examined. A total of 529 periapical images and 551 panoramic images were collected from the patients for a deep learning-based model. Based on radiographic peri-implant alveolar bone pattern, implant outcome was divided into three categories: implant failure with marginal bone loss, implant failure without marginal bone loss, and implant success. We extracted features using a deep convolutional neural network (CNN) and built a hybrid model to combine periapical and panoramic images. A comparison among three categories of receiver operating characteristic (ROC) curves was performed. The diagnostic accuracy, precision, recall and F1-score of the dataset were assessed. Results: Our model achieved an AUC (area under the ROC curve) of 0.972 for failure with marginal bone loss, 0.947 for failure without marginal bone loss and 0.975 for success. In all conditions, for periapical images alone, the diagnostic accuracy was 78.6%; the precision was 0.84, recall was 0.73, and F1-score was 0.75. For panoramic images alone, the diagnostic accuracy was 78.7%; the precision was 0.87, recall was 0.63, and F1-score was 0.66. Both periapical and panoramic images were used in our novel method, and the prediction accuracy was 87%. The precision was 0.85, recall was 0.88, and F1-score was 0.85.Conclusions: The deep learning model learned features from periapical and panoramic images and predicted the occurrence of implant failure effectively. It might facilitate early clinical intervention for potential dental implant failures.