A computer vision model for automated kidney stone segmentation and evaluation of its performance vs surgeons
The computer vision model demonstrates good performance for task-specific stone segmentation evaluation during URS. The segmentation performance of the model was similar to the segmentation performance of expert surgeons, demonstrating the feasibility of its real-time intra-operative utilisation.
Authors
- İpek Oğuz (ORCID: https://orcid.org/0000-0002-1403-2420)
- Nicholas Kavoussi (ORCID: https://orcid.org/0000-0001-7202-0412)
- Tatsuki Koyama (ORCID: https://orcid.org/0000-0002-8908-9165)
- Daiwei Lu (ORCID: https://orcid.org/0000-0001-6693-4445)
- Ekamjit S. Deol (ORCID: https://orcid.org/0000-0001-9840-9581)
Institutions
- Vanderbilt University (US)
- Saint Louis University (US)
- Vanderbilt University Medical Center (US)
Publication Details
- Journal
- British Journal of Urology
- Published
- 2025-09-25
- DOI
- https://doi.org/10.1111/bju.70001
- Citations
- 1
- Primary Topic
- Kidney Stones and Urolithiasis Treatments
- Type
- article
- Field-Weighted Citation Impact
- 1.52
Funders
- National Institutes of Health
- National Institute of Diabetes and Digestive and Kidney Diseases