Automatic deep learning detection of overhanging restorations in bitewing radiographs

Abstract

Objective This study aimed to assess the effectiveness of deep convolutional neural network (CNN) algorithms in detecting and segmentation of overhanging dental restorations in bitewing radiographs. Method and materials A total of 1160 anonymized bitewing radiographs were used to progress the Artificial Intelligence (AI) system) for the detection and segmentation of overhanging restorations. The data was then divided into three groups: 80% for training (930 images, 2399 labels), 10% for validation (115 images, 273 labels), and 10% for testing (115 images, 306 labels). A CNN model known as you only look once (YOLOv5) was trained to detect overhanging restorations in bitewing radiographs. After utilizing the remaining 115 radiographs to evaluate the efficacy of the proposed CNN model, the accuracy, sensitivity, precision, F1 score, and area under the receiver operating characteristic curve (AUC) were computed. Results The model demonstrated a precision of 90.9%, a sensitivity of 85.3%, and an F1 score of 88.0%. Furthermore, the model achieved an AUC of 0.859 on the Receiver Operating Characteristic (ROC) curve. The mean average precision (mAP) at an intersection over a union (IoU) threshold of 0.5 was notably high at 0.87. Conclusion The findings suggest that deep CNN algorithms are highly effective in the detection and diagnosis of overhanging dental restorations in bitewing radiographs. The high levels of precision, sensitivity, and F1 score, along with the significant AUC and mAP values, underscore the potential of these advanced deep learning techniques in revolutionizing dental diagnostic procedures.