Limitations of panoramic radiographs in predicting mandibular wisdom tooth extraction and the potential of deep learning models to overcome them - Atsushi Danjo; Chiaki Kuwada; Reona Aijima; Asana Kamohara; Motoki Fukuda; Yoshiko Ariji; Eiichiro Ariji; Yoshio Yamashita (Scientific Reports)
Abstract Surgeons routinely interpret preoperative radiographic images for estimating the shape and position of the tooth prior to performing tooth extraction. In this study, we aimed to predict the difficulty of lower wisdom tooth extraction using only panoramic radiographs. Difficulty was evaluated using the modified Parant score. Two oral surgeons (a specialist and a clinical resident) predicted the difficulty level of the test data. This study also aimed to evaluate the performance of a deep learning model in predicting the necessity for tooth separation or bone removal during wisdom tooth extraction. Two convolutional neural networks (AlexNet and VGG-16) were created and trained using panoramic X-ray images. Both surgeons interpreted the same images and classified them into three groups. The accuracies for humans were 54.4% for both surgeons, 57.7% for AlexNet, and 54.4% for VGG-16. These results indicate that accurately predict the difficulty of wisdom teeth extraction using panoramic radiographs alone is challenging. However, AlexNet and VGG-16 had sensitivities of more than 90% for crown and root separation. The predictive ability of our proposed model is equivalent to that of an oral surgery specialist, and a recall value > 90% makes it suitable for screening in clinical settings.