Crossmark

Integrating body composition analysis and machine learning for non-invasive identification of metabolic dysfunction-associated fatty liver disease: a large-scale health examination-based study

Crossref DOI link: https://doi.org/10.1038/s41598-026-37852-w

Published Online: 2026-02-03

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

He, Yaxuan

Cao, Yu

Chen, Zekai

Xiang, Rong

Wang, Fang
Funding

Funding for this research was provided by:

Natural Science Foundation of Hunan Province (2024JJ5520)

Changsha Municipal Natural Science Foundation (kq2403054)

Hunan Provincial Program for Young Key Teachers in Universities (20240101-20261230)
License Information

Text and Data Mining valid from 2026-02-03

Accepted Manuscript valid from 2026-02-03
More Information

Article History

Received: 6 October 2025

Accepted: 27 January 2026

First Online: 3 February 2026

Declarations

:

: The authors declare no competing interests.

: This study was approved by the Ethics Committee of The Third Xiangya Hospital, Central South University (Approval Number: 225546). As this was a retrospective study using anonymized health examination data, the requirement for informed consent was waived by the ethics committee.

: Not applicable.

Document is current

Any future updates will be listed below