Artificial intelligence for predicting interstitial fibrosis and tubular atrophy using diagnostic ultrasound imaging and biomarkers
Our artificial intelligence-based approach to IFTA classification demonstrated high accuracy and AUROC across various ML models. By leveraging patient biomarkers alone, this method offers a non-invasive and robust tool for early CKD assessment, demonstrating that biomarkers alone may suffice for accurate predictions without the added complexity of image-derived features.
Authors
- Yen‐Chung Lin (ORCID: https://orcid.org/0000-0002-8981-8671)
- Kwan‐Dun Wu (ORCID: https://orcid.org/0000-0002-3339-1509)
- Che-Chou Shen (ORCID: https://orcid.org/0000-0002-4542-4195)
- Zhen-Yi Tang
- Chia‐Te Liao (ORCID: https://orcid.org/0000-0003-2550-9607)
- Chung-Yi Cheng
- Cai Zheng
- Ting-Wei Chang (ORCID: https://orcid.org/0000-0002-2868-7313)
- C. S. Tsai
Institutions
- National Taiwan University of Science and Technology (TW)
- National Taiwan University (TW)
- Taipei Medical University-Shuang Ho Hospital (TW)
- Taipei Medical University (TW)
Publication Details
- Journal
- BMJ Health & Care Informatics
- Published
- 2025-03-01
- DOI
- https://doi.org/10.1136/bmjhci-2024-101192
- Citations
- 4
- Primary Topic
- Systemic Sclerosis and Related Diseases
- Type
- article
- Field-Weighted Citation Impact
- 5.89
Funders
- Taipei Medical University
- National Taiwan University
- National Taiwan University of Science and Technology
- National Science and Technology Council