A machine learning-based risk prediction model for diabetic oral ulceration
The SVC model demonstrated superior performance and stability, making it suitable for predicting DOU occurrence and development in diabetic patients. This study's innovation lies in the comprehensive evaluation of multiple factors, including oral examinations, physiological indicators, self-management capabilities, and economic factors, to facilitate efficient DOU screening. The findings highlight the potential of ML in improving diagnostic accuracy and enabling timely interventions for DOU, ultimately contributing to better clinical management and patient outcomes. Future research should focus on validating the model across larger, multicenter cohorts and further exploring the long-term impact of ML-guided interventions on DOU management.
Authors
- Guoyao Tang (ORCID: https://orcid.org/0000-0003-1150-5613)
- Bingqian Wang (ORCID: https://orcid.org/0000-0003-4020-2486)
- Chenglong Yang
- Wen Li (ORCID: https://orcid.org/0000-0002-8805-2934)
- Xiao‐Ling Wang
- Xin Feng
- Jie Wei (ORCID: https://orcid.org/0000-0002-3114-546X)
- Shuyan Gu (ORCID: https://orcid.org/0000-0001-8368-4855)
- Chen Hanbei
- J Li
- Zhu Zhenqi
Institutions
- Shanghai Jiao Tong University (CN)
- XinHua Hospital (CN)
- Government of Jiangsu Province (CN)
- Tongren Hospital (CN)
- Primary HealthCare (MT)
- Suzhou Traditional Chinese Medicine Hospital (CN)
Publication Details
- Journal
- BMC Oral Health
- Published
- 2025-05-22
- DOI
- https://doi.org/10.1186/s12903-025-06096-x
- Citations
- 1
- Primary Topic
- Oral microbiology and periodontitis research
- Type
- article
- Field-Weighted Citation Impact
- 1.47
Funders
- National Natural Science Foundation of China
- Shanghai Jiao Tong University
- School of Medicine, Shanghai Jiao Tong University