Success Of Artificial Intelligence System In Determining Alveolar Bone Loss From Dental Panoramic Radiography Images

Abstract

Objectives: This study aims to detect alveolar bone loss from dental panoramic radiography images by using an artificial intelligence (AI) system. Materials and Methods: A total of 2276 panoramic radiography images were evaluated. Of these, 1137 were of bone loss cases and 1139 were of periodontally healthy cases. This dataset is divided into training (n = 1856), validation (n = 210), and testing (n = 210) sets. All images were resized to 1472x718 pixels before training. A random sequence was created using the open-source Python programming language and OpenCV, NumPy, Pandas, and Matplotlib libraries. A pretrained Google Net Inception v3 convolutional neural network (CNN) was used for preprocessing, and the datasets were trained using transfer learning. The diagnostic performance was evaluated using a confusion matrix in terms of the sensitivity, specificity, precision, accuracy, and F1 score. Results: Of 105 cases with bone loss, the CNN system detected 99 with sensitivity, specificity, precision, accuracy, and F1 score of 0.94, 0.88, 0.89, 0.91, and 0.91, respectively. Conclusions: The CNN system successfully determines periodontal bone loss. Therefore, it can be used to facilita