Interpretable machine learning model integrating clinical and elastosonographic features to detect renal fibrosis in Asian patients with chronic kidney disease.

Journal: Journal of nephrology

PMID: 38315278

Abstract

BACKGROUND: Non-invasive renal fibrosis assessment is critical for tailoring personalized decision-making and managing follow-up in patients with chronic kidney disease (CKD). We aimed to exploit machine learning algorithms using clinical and elastosonographic features to distinguish moderate-severe fibrosis from mild fibrosis among CKD patients.

Authors

Ziman Chen

Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong. chenzm27@mail3.sysu.edu.cn.
Yingli Wang

Ultrasound Department, EDAN Instruments, Inc., Shenzhen, China.
Michael Tin Cheung Ying

Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong. michael.ying@polyu.edu.hk.
Zhongzhen Su

Department of Ultrasound, Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China.

Keywords

Adult Aged Asian People Biopsy Elasticity Imaging Techniques Female Fibrosis Humans Kidney Machine Learning Male Middle Aged Prospective Studies Renal Insufficiency, Chronic Severity of Illness Index

External Resources

View on PubMed Access via DOI PubMed (38315278)

Interpretable machine learning model integrating clinical and elastosonographic features to detect renal fibrosis in Asian patients with chronic kidney disease.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals