The value of unsupervised machine learning algorithms based on CT and MRI for predicting sarcopenia
The findings indicate that CT-based unsupervised machine learning models outperform their MRI-based counterparts, with the CT-based Otsu and GMM models showing exceptional efficacy in sarcopenia prediction, as evidenced by AUC values exceeding 0.95.
Authors
- Yunzhu Wu (ORCID: https://orcid.org/0000-0002-4511-762X)
- Yijin Wang (ORCID: https://orcid.org/0000-0001-6149-2609)
- Qiu Bi
- Yang Song (ORCID: https://orcid.org/0000-0003-1283-1672)
- Guoli Bi
- Chengxiu Zhang
- Gang Wang (ORCID: https://orcid.org/0000-0002-7266-2412)
- Xiaolong Liu (ORCID: https://orcid.org/0000-0002-3096-4981)
- Guang Yang (ORCID: https://orcid.org/0000-0001-7344-7733)
- Xiarong Gong
- Huayan Zuo
- Qiyang Wang
Institutions
- Kunming University of Science and Technology (CN)
- Nanjing University of Information Science and Technology (CN)
- Kunming Medical University (CN)
- First People's Hospital of Yunnan Province (CN)
- The First Hospital of Kunming (CN)
- Siemens (China) (CN)
- East China Normal University (CN)
Publication Details
- Journal
- Frontiers in Public Health
- Published
- 2025-09-25
- DOI
- https://doi.org/10.3389/fpubh.2025.1649400
- Citations
- 1
- Primary Topic
- Nutrition and Health in Aging
- Type
- article
- Field-Weighted Citation Impact
- 1.16
Funders
- Kunming Medical University