Natural language processing for kidney ultrasound analysis: correlating imaging reports with chronic kidney disease diagnosis
State-of-the-art NLP models can accurately extract CKD-related features from ultrasound reports, with the potential of providing a scalable tool for early detection and risk stratification. Future research should focus on validating these models across different healthcare systems.
Authors
- Farrukh M. Koraishy (ORCID: https://orcid.org/0000-0001-6974-5674)
- R. Cunningham
- Chenlu Wang (ORCID: https://orcid.org/0009-0007-6870-5401)
- Puja Sakhuja (ORCID: https://orcid.org/0000-0001-7286-5941)
- Janos Hajagos (ORCID: https://orcid.org/0000-0001-6251-9586)
- Ritwik Banerjee (ORCID: https://orcid.org/0000-0003-0336-0258)
- Ruqiyya Bano
- Hira Tahir
- Harry Kuperstein
- Hamza Malick
Institutions
- Stony Brook School (US)
- Stony Brook University (US)
Publication Details
- Journal
- Renal Failure
- Published
- 2025-08-04
- DOI
- https://doi.org/10.1080/0886022x.2025.2539938
- Primary Topic
- Renal and Vascular Pathologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00