Interpretable machine learning prediction of extracorporeal shock wave lithotripsy outcomes for urinary stones: a retrospective cohort study
We present a transparent, interpretable ML framework for ESWL outcome prediction. While the model aligns with clinical reasoning and offers a foundation for trustworthy artificial intelligence, its current low sensitivity limits immediate standalone clinical utility for ruling out ESWL failure. The framework highlights the imperative for future work to improve sensitivity through richer datasets and prospective validation before integration into clinical pathways.
Authors
- Mohammed A. Ebrahim
- Morshed Salah (ORCID: https://orcid.org/0000-0001-6765-8489)
- Hatem Kamkoum (ORCID: https://orcid.org/0000-0002-6925-2797)
- Hossameldin Alnawasra
- Faisal Ahmed (ORCID: https://orcid.org/0000-0003-0015-6008)
- Salvan Alhabash (ORCID: https://orcid.org/0009-0006-7051-2759)
- Maged Al-Ghashmi (ORCID: https://orcid.org/0009-0004-3280-2398)
- Abdoulhafid Elmogassabi
- Abu Baker
- Mohamed Abdelkareem
Institutions
- Hamad General Hospital (QA)
- Hamad Medical Corporation (QA)
- Ibb University (YE)
Publication Details
- Journal
- Archivio Italiano di Urologia e Andrologia
- Published
- 2025-12-24
- DOI
- https://doi.org/10.4081/aiua.2025.14333
- Primary Topic
- Kidney Stones and Urolithiasis Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00