Danjo Atsushi, Kuwada Chiaki, Aijima Reona, Kamohara Asana, Fukuda Motoki, Ariji Yoshiko, Ariji Eiichiro, Yamashita Yoshio
Department of Oral and Maxillofacial Surgery, Faculty of Medicine, Saga University, 5-1-1 Nabeshima, Saga, 849-8501, Japan.
Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry, Nagoya, Japan.
Sci Rep. 2024 Dec 28;14(1):30806. doi: 10.1038/s41598-024-81153-z.
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.
外科医生在进行拔牙手术前，通常会解读术前的影像学图像，以评估牙齿的形状和位置。在本研究中，我们旨在仅使用全景X光片来预测下颌智齿拔除的难度。使用改良的Parant评分来评估难度。两名口腔外科医生（一名专家和一名临床住院医生）对测试数据的难度水平进行了预测。本研究还旨在评估深度学习模型在预测智齿拔除过程中牙齿分离或去骨必要性方面的性能。使用全景X光图像创建并训练了两个卷积神经网络（AlexNet和VGG - 16）。两名外科医生都解读了相同的图像，并将其分为三组。两名外科医生的人类准确率均为54.4%，AlexNet为57.7%，VGG - 16为54.4%。这些结果表明，仅使用全景X光片准确预测智齿拔除的难度具有挑战性。然而，AlexNet和VGG - 16对牙冠和牙根分离的敏感度超过90%。我们提出的模型的预测能力与口腔外科专家相当，召回值>90%使其适用于临床筛查。
