Deep Learning-based Diagnosis of Cervical Burnout and Interproximal Caries in Bitewing Radiographs

Background: This study aimed to evaluate the success of deep learning-based convolutional neural networks (CNN) and Residual Neural Network-34 (ResNet34) in classifying and detecting cervical burnout and interproximal caries on bitewing radiographs. Methods: Within the scope of the study, a dataset consisting of 454 bitewing radiographs, free of noise and artifacts, was labeled by two dentists with LabelImg software. A 32-layer CNN model (614 interproximal caries, 402 cervical burnout) was created for classification, and a ResNet34 model was created for object detection. The images were resized to 300x300 pixels, and the datasets were divided into 80% training and 20% testing. Performance metrics included sensitivity, specificity, precision, accuracy, and F1 score. Results: The classification model achieved 93.14% accuracy, 86.42% sensitivity, and 97.56% specificity, while the object detection model gave 81.74% accuracy and 0.82 mAP values ​​at 0.5 IoU. The data showed that CNN models were successful in classifying cervical burnout and interproximal caries. The data also showed that ResNet-34 models were successful in detecting cervical burnout and interproximal caries. Conclusion: Despite a limited dataset, CNN models showed successful results in classifying cervical burnout and interproximal caries on bitewing radiographs.