Diagnostic charting of panoramic radiography using deep-learning artificial intelligence system

Objectives The goal of this study was to develop and evaluate the performance of a new deep-learning (DL) artificial intelligence (AI) model for diagnostic charting in panoramic radiography. Methods One thousand eighty-four anonymous dental panoramic radiographs were labeled by two dento-maxillofacial radiologists for ten different dental situations: crown, pontic, root-canal treated tooth, implant, implant-supported crown, impacted tooth, residual root, filling, caries, and dental calculus. AI Model CranioCatch, developed in Eskisehir, Turkey and based on a deep CNN method, was proposed to be evaluated. A Faster R-CNN Inception v2 (COCO) model implemented with the TensorFlow library was used for model development. The assessment of AI model performance was evaluated with sensitivity, precision, and F1 scores. Results When the performance of the proposed AI model for detecting dental conditions in panoramic radiographs was evaluated, the best sensitivity values were obtained from the crown, implant, and impacted tooth as 0.9674, 0.9615, and 0.9658, respectively. The worst sensitivity values were obtained from the pontic, caries, and dental calculus, as 0.7738, 0.3026, and 0.0934, respectively. The best precision values were obtained from pontic, implant, implant-supported crown as 0.8783, 0.9259, and 0.8947, respectively. The worst precision values were obtained from residual root, caries, and dental calculus, as 0.6764, 0.5096, and 0.1923, respectively. The most successful F1 Scores were obtained from the implant, crown, and implant-supported crown, as 0.9433, 0.9122, and 0.8947, respectively. Conclusion The proposed AI model has promising results at detecting dental conditions in panoramic radiographs, except for caries and dental calculus. Thanks to the improvement of AI models in all areas of dental radiology, we predict that they will help physicians in panoramic diagnosis and treatment planning, as well as digital-based student education, especially during the pandemic period.