Enhancing furcation involvement classification on panoramic radiographs with vision transformers
DL algorithms can automatically classify FI using readily accessible panoramic images. These findings demonstrate that ViT outperforms the tested traditional models, highlighting the potential of transformer-based approaches to significantly advance image classification. This approach is also expected to reduce both the radiation dose and the financial burden on patients while simultaneously improving diagnostic precision.
Authors
- Hong Sheng Zhao (ORCID: https://orcid.org/0000-0002-1092-6443)
- Li Wen (ORCID: https://orcid.org/0000-0002-1498-3103)
- Enting Guo (ORCID: https://orcid.org/0000-0002-3462-1505)
- Wenlei Wu
- Xu Liu (ORCID: https://orcid.org/0000-0002-8780-5455)
- Jie Yang (ORCID: https://orcid.org/0000-0002-5465-6772)
- Weibin Sun (ORCID: https://orcid.org/0000-0001-5250-4022)
- Xuan Zhang (ORCID: https://orcid.org/0000-0003-0291-4458)
Institutions
- North University of China (CN)
- University of Aizu (JP)
- Stomatology Hospital (CN)
- Nanjing Medical University (CN)
- Nanjing University (CN)
Publication Details
- Journal
- BMC Oral Health
- Published
- 2025-01-29
- DOI
- https://doi.org/10.1186/s12903-025-05431-6
- Citations
- 13
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 23.28
Funders
- National Natural Science Foundation of China