Optimizing dental implant identification using deep learning leveraging artificial data.

ABSTRACT: This study aims to evaluate the potential enhancement in implant classification performance achieved by incorporating artificially generated images of commercially available products into a deep learning process of dental implant classification using panoramic X-ray images. To supplement an existing dataset of 7,946 in vivo dental implant images, a three-dimensional scanner was employed to create implant surface models. Subsequently, implant surface models were used to generate two-dimensional X-ray images, which were compiled along with original images to create a comprehensive dataset. Images of 10 types of implants were classified using ResNet50 into the following datasets: (A) images of implants captured in vivo, (B) artificial implant images generated without background adjustments, a

SUBMITTER: Sukegawa S 

PROVIDER: S-EPMC11779872 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature