Enhancing diabetic foot ulcer prediction with machine learning: A focus on Localized examinations
The predictive model, developed through AutoML and grounded in local foot examinations, has proven to be a robust and practical instrument for the screening, prediction, and diagnosis of DFU risk. This model not only aids medical practitioners in the identification of potential DFU cases but also plays a pivotal role in mitigating the progression towards adverse outcomes. And the recent successful external validation of our DFU risk prediction model marks a crucial advancement, indicating its readiness for clinical application. This validation reinforces the model's efficacy as an accessible and reliable tool for early DFU risk assessment, thereby facilitating prompt intervention strategies and enhancing overall patient outcomes.
Authors
- Li Wen (ORCID: https://orcid.org/0000-0002-1498-3103)
- Bingqian Wang (ORCID: https://orcid.org/0000-0003-4020-2486)
- Xiao‐Ling Wang
- Yao Dai (ORCID: https://orcid.org/0000-0002-7194-1743)
- Shengmei Zhu (ORCID: https://orcid.org/0000-0002-2829-9978)
- Shuyan Gu (ORCID: https://orcid.org/0000-0001-8368-4855)
- Li Jutang
- Chen Hanbei
- Qin Chenjie
Institutions
- Shanghai Jiao Tong University (CN)
- XinHua Hospital (CN)
- Tongren Hospital (CN)
- Suzhou Traditional Chinese Medicine Hospital (CN)
- Shanghai First People's Hospital (CN)
- Nanjing University (CN)
Publication Details
- Journal
- Heliyon
- Published
- 2024-09-19
- DOI
- https://doi.org/10.1016/j.heliyon.2024.e37635
- Citations
- 12
- Primary Topic
- Diabetic Foot Ulcer Assessment and Management
- Type
- article
- Field-Weighted Citation Impact
- 4.29
Funders
- National Natural Science Foundation of China
- Shanghai Municipal Education Commission
- Shanghai Jiao Tong University
- School of Medicine, Shanghai Jiao Tong University