Two-step deep learning models for detection and identification of the manufacturers and types of dental implants on panoramic radiographs.

Autor: Ariji Y; Department of Oral Radiology, Osaka Dental University, 1-5-17, Otemae, Chuo-ku, Osaka, 540-0008, Japan. ariji-y@cc.osaka-dent.ac.jp., Kusano K; Department of Oral Implantology, Osaka Dental University, Osaka, Japan., Fukuda M; Department of Oral Radiology, Osaka Dental University, 1-5-17, Otemae, Chuo-ku, Osaka, 540-0008, Japan., Wakata Y; Department of Oral Implantology, Osaka Dental University, Osaka, Japan., Nozawa M; Department of Oral Radiology, Osaka Dental University, 1-5-17, Otemae, Chuo-ku, Osaka, 540-0008, Japan., Kotaki S; Department of Oral Radiology, Osaka Dental University, 1-5-17, Otemae, Chuo-ku, Osaka, 540-0008, Japan., Ariji E; Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University, Nagoya, Japan., Baba S; Department of Oral Implantology, Osaka Dental University, Osaka, Japan.
Jazyk: angličtina
Zdroj: Odontology [Odontology] 2024 Aug 29. Date of Electronic Publication: 2024 Aug 29.
DOI: 10.1007/s10266-024-00989-z
Abstrakt: 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.
(© 2024. The Author(s), under exclusive licence to The Society of The Nippon Dental University.)
Databáze: MEDLINE
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