Enhanced accuracy and stability in automated intra-pancreatic fat deposition monitoring of type 2 diabetes mellitus using Dixon MRI and deep learning
The DLR model demonstrated superior performance over radiologists, providing a more efficient, accurate and stable method for monitoring IPFD and predicting the risk of T2DM and pre-diabetes. This enables IPFD assessment to potentially serve as an early biomarker for T2DM, providing richer clinical information for disease progression and management.
Authors
- Haodong Qin (ORCID: https://orcid.org/0009-0004-1659-0233)
- Wenxi Liu (ORCID: https://orcid.org/0000-0002-3630-6322)
- Zhangnan Zhong
- Qiuyi Chen (ORCID: https://orcid.org/0009-0009-4792-5266)
- Zhujing Li
- Yueyao Chen (ORCID: https://orcid.org/0000-0002-6225-3359)
- Zhongxian Pan (ORCID: https://orcid.org/0000-0002-3332-4058)
- Fanqi Meng
- Haiwei Lin
- Bingsheng Huang (ORCID: https://orcid.org/0000-0001-7123-2907)
- Wen‐Sheng Huang (ORCID: https://orcid.org/0009-0001-1194-212X)
- Junfeng Li
Institutions
- Guangzhou University of Chinese Medicine (CN)
- Sun Yat-sen University (CN)
- Shenzhen University (CN)
- University Town of Shenzhen (CN)
- The Seventh Affiliated Hospital of Sun Yat-sen University (CN)
- Siemens (China) (CN)
Publication Details
- Journal
- Abdominal Radiology
- Published
- 2025-01-22
- DOI
- https://doi.org/10.1007/s00261-025-04804-3
- Citations
- 3
- Primary Topic
- Liver Disease Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 4.06
Funders
- National Natural Science Foundation of China
- Guangdong Medical Research Foundation
- Shenzhen-Hong Kong Institute of Brain Science
- Science and Technology Planning Project of Shenzhen Municipality