Machine Learning–Based Biomarker Identification for Early Diagnosis of Metabolic Dysfunction–Associated Steatotic Liver Disease
These findings indicate that assessing a variety of biomarkers, across demographic, metabolic, lipid, and standard biochemistry categories, may provide valuable initial insights for diagnosing patients for MASLD and hepatic fibrosis.
Authors
- A. Wayne Orr (ORCID: https://orcid.org/0000-0002-2377-213X)
- Diensn Xing
- Christopher G. Kevil (ORCID: https://orcid.org/0000-0003-0863-7260)
- Mohammad Alfrad Nobel Bhuiyan (ORCID: https://orcid.org/0000-0002-6011-2624)
- Oren Rom (ORCID: https://orcid.org/0000-0002-9272-086X)
- John A. Vanchiere (ORCID: https://orcid.org/0000-0001-6863-4323)
- Md. Shenuarin Bhuiyan (ORCID: https://orcid.org/0000-0003-0073-3071)
- Steven A. Conrad (ORCID: https://orcid.org/0000-0002-4014-969X)
- Jolie Boullion
- Md. Ismail Hossain (ORCID: https://orcid.org/0000-0002-6781-5891)
- Akshat Agrawal
- Amanda Husein
Institutions
- Louisiana State University in Shreveport (US)
- Louisiana State University Health Sciences Center Shreveport (US)
Publication Details
- Journal
- The Journal of Clinical Endocrinology & Metabolism
- Published
- 2025-02-21
- DOI
- https://doi.org/10.1210/clinem/dgaf111
- Citations
- 4
- Primary Topic
- Liver Disease Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 5.42