Exploring unsupervised learning techniques for early detection of myocardial ischemia in type 2 diabetes
Although the unsupervised clustering approach revealed differences in various clinical and imaging characteristics, no significant differences were observed in ischemic burden, cavity size, involved segments, or EDV.
Authors
- Bing Liu (ORCID: https://orcid.org/0000-0002-0809-3358)
- Yan-jie Hou
- Han Xiao (ORCID: https://orcid.org/0000-0003-1978-9593)
- Hui Qi (ORCID: https://orcid.org/0000-0002-1545-5548)
- Sijin Li (ORCID: https://orcid.org/0000-0003-0361-1248)
- Zhifang Wu (ORCID: https://orcid.org/0000-0002-4122-4786)
- Wu Ping (ORCID: https://orcid.org/0000-0001-6215-4847)
- Xiuyun Yang
Institutions
- Shanxi Medical University (CN)
- First Hospital of Shanxi Medical University (CN)
- Shandong First Medical University (CN)
Publication Details
- Journal
- Frontiers in Endocrinology
- Published
- 2025-10-13
- DOI
- https://doi.org/10.3389/fendo.2025.1668516
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00