A Deep Learning-Based Approach to Detect Lamina Dura Loss on Periapical Radiographs

This study aimed to develop a custom artificial intelligence (AI) model for detecting lamina dura (LD) loss around the roots of anterior and posterior teeth on intraoral periapical radiographs. A total of 701 periapical radiographs of the anterior and posterior regions retrieved from the Dentomaxillofacial Radiology archives were reviewed. Images were cropped to include only the teeth exhibiting LD loss and those without LD loss, which were labeled as "1" and "0," respectively. The dataset was diversified using image preprocessing and data augmentation techniques. Among the radiographs, 72% were used for training, 18% for validation, and 10% for testing. A custom AI model, consisting of 4 blocks and 49 layers, with a total of 21.2 million parameters, was developed using the TensorFlow library and residual blocks introduced in ResNet architecture. Sensitivity, specificity, accuracy, precision, F1 score, and kappa (κ) coefficients (for intra-observer agreement) were calculated to evaluate the performance of the AI model. When applied to a test set of 71 images, the AI model showed good performance in detecting LD loss, achieving an average sensitivity of 0.730, specificity of 0.706, accuracy of 0.718, precision of 0.730, and an F1 score of 0.730, regardless of the dental region. This study represents the first known application of an AI algorithm tailored to detect LD loss on periapical radiographs. The developed AI model could aid clinicians in making accurate diagnosis and help prevent misdiagnosis.