Ariji Yoshiko, Kusano Kaoru, Fukuda Motoki, Wakata Yo, Nozawa Michihito, Kotaki Shinya, Ariji Eiichiro, Baba Shunsuke
Department of Oral Radiology, Osaka Dental University, 1-5-17, Otemae, Chuo-ku, Osaka, 540-0008, Japan.
Department of Oral Implantology, Osaka Dental University, Osaka, Japan.
Odontology. 2025 Apr;113(2):788-798. doi: 10.1007/s10266-024-00989-z. Epub 2024 Aug 29.
The purpose of this study is to develop two-step deep learning models that can automatically detect implant regions on panoramic radiographs and identify several types of implants. A total of 1,574 panoramic radiographs containing 3675 implants were included. The implant manufacturers were Kyocera, Dentsply Sirona, Straumann, and Nobel Biocare. Model A was created to detect oral implants and identify the manufacturers using You Only Look Once (YOLO) v7. After preparing the image patches that cropped the implant regions detected by model A, model B was created to identify the implant types per manufacturer using EfficientNet. Model A achieved very high performance, with recall of 1.000, precision of 0.979, and F1 score of 0.989. It also had accuracy, recall, precision, and F1 score of 0.98 or higher for the classification of the manufacturers. Model B had high classification metrics above 0.92, exception for Nobel's class 2 (Parallel). In this study, two-step deep learning models were built to detect implant regions, identify four manufacturers, and identify implant types per manufacturer.
本研究的目的是开发两步深度学习模型，该模型能够自动检测全景X线片中的种植体区域并识别几种类型的种植体。共纳入了1574张包含3675个种植体的全景X线片。种植体制造商包括京瓷、登士柏西诺德、士卓曼和诺贝尔生物科技。模型A使用You Only Look Once（YOLO）v7创建，用于检测口腔种植体并识别制造商。在准备好裁剪模型A检测到的种植体区域的图像块后，模型B使用EfficientNet创建，用于按制造商识别种植体类型。模型A表现出非常高的性能，召回率为1.000，精确率为0.979，F1分数为0.989。其制造商分类的准确率、召回率、精确率和F1分数也均在0.98或更高。模型B除了诺贝尔2类（平行）外，分类指标均高于0.92。在本研究中，构建了两步深度学习模型来检测种植体区域、识别四个制造商并按制造商识别种植体类型。
