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...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-87579-3 |
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| author | Shintaro Sukegawa Kazumasa Yoshii Takeshi Hara Futa Tanaka Yoshihiro Taki Yuta Inoue Katsusuke Yamashita Fumi Nakai Yasuhiro Nakai Ryo Miyazaki Takanori Ishihama Minoru Miyake |
| author_facet | Shintaro Sukegawa Kazumasa Yoshii Takeshi Hara Futa Tanaka Yoshihiro Taki Yuta Inoue Katsusuke Yamashita Fumi Nakai Yasuhiro Nakai Ryo Miyazaki Takanori Ishihama Minoru Miyake |
| author_sort | Shintaro Sukegawa |
| collection | DOAJ |
| description | 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, and (C) implant images derived from in vivo images and generated with background adjustments. The classification accuracy was 0.8888 for dataset A, 0.903 for dataset B, and 0.9146 for dataset C. Notably, dataset C demonstrated the highest performance and exhibited the optimal feature distribution. In the context of deep learning classifiers for dental implants using panoramic X-ray images, incorporating artificially generated X-ray images—designed to mirror the appearance of human body implants—proved to be the most beneficial in enhancing the performance of the classification model. |
| format | Article |
| id | doaj-art-58e50554d71f49f5a953632700e3a2c7 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-58e50554d71f49f5a953632700e3a2c72025-02-02T12:16:28ZengNature PortfolioScientific Reports2045-23222025-01-0115111410.1038/s41598-025-87579-3Optimizing dental implant identification using deep learning leveraging artificial dataShintaro Sukegawa0Kazumasa Yoshii1Takeshi Hara2Futa Tanaka3Yoshihiro Taki4Yuta Inoue5Katsusuke Yamashita6Fumi Nakai7Yasuhiro Nakai8Ryo Miyazaki9Takanori Ishihama10Minoru Miyake11Department of Oral and Maxillofacial Surgery, Faculty of Medicine, Kagawa UniversityDepartment of Intelligence Science and Engineering, Graduate School of Natural Science and Technology, Gifu UniversityDepartment of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu UniversityDepartment of Intelligence Science and Engineering, Graduate School of Natural Science and Technology, Gifu UniversityDepartment of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu UniversityDepartment of Intelligence Science and Engineering, Graduate School of Natural Science and Technology, Gifu UniversityPolytechnic Center KagawaDepartment of Oral and Maxillofacial Surgery, Faculty of Medicine, Kagawa UniversityDepartment of Oral and Maxillofacial Surgery, Faculty of Medicine, Kagawa UniversityDepartment of Oral and Maxillofacial Surgery, Faculty of Medicine, Kagawa UniversityDepartment of Oral and Maxillofacial Surgery, Faculty of Medicine, Kagawa UniversityDepartment of Oral and Maxillofacial Surgery, Faculty of Medicine, Kagawa UniversityAbstract 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, and (C) implant images derived from in vivo images and generated with background adjustments. The classification accuracy was 0.8888 for dataset A, 0.903 for dataset B, and 0.9146 for dataset C. Notably, dataset C demonstrated the highest performance and exhibited the optimal feature distribution. In the context of deep learning classifiers for dental implants using panoramic X-ray images, incorporating artificially generated X-ray images—designed to mirror the appearance of human body implants—proved to be the most beneficial in enhancing the performance of the classification model.https://doi.org/10.1038/s41598-025-87579-3Dental implantsDeep learningPanoramic X-rayArtificial image generationClassification accuracy |
| spellingShingle | Shintaro Sukegawa Kazumasa Yoshii Takeshi Hara Futa Tanaka Yoshihiro Taki Yuta Inoue Katsusuke Yamashita Fumi Nakai Yasuhiro Nakai Ryo Miyazaki Takanori Ishihama Minoru Miyake Optimizing dental implant identification using deep learning leveraging artificial data Scientific Reports Dental implants Deep learning Panoramic X-ray Artificial image generation Classification accuracy |
| title | Optimizing dental implant identification using deep learning leveraging artificial data |
| title_full | Optimizing dental implant identification using deep learning leveraging artificial data |
| title_fullStr | Optimizing dental implant identification using deep learning leveraging artificial data |
| title_full_unstemmed | Optimizing dental implant identification using deep learning leveraging artificial data |
| title_short | Optimizing dental implant identification using deep learning leveraging artificial data |
| title_sort | optimizing dental implant identification using deep learning leveraging artificial data |
| topic | Dental implants Deep learning Panoramic X-ray Artificial image generation Classification accuracy |
| url | https://doi.org/10.1038/s41598-025-87579-3 |
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