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Predicting Postoperative Outcomes in Pediatric Ureteroscopy Using Machine Learning and Explainable AI—EAU Endourology Vision-AI Study
0
Zitationen
20
Autoren
2026
Jahr
Abstract
ML models demonstrated high accuracy in predicting adverse postoperative outcomes in pediatric ureteroscopy, with ensemble methods showing the best performance. Integration with XAI enhanced interpretability, supporting clinical decision-making. These findings underscore the potential of ML and XAI to inform personalized treatment strategies, though further prospective validation is needed to develop robust, generalizable predictive tools.
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Autoren
Institutionen
- European Association of Urology(NL)
- Azienda Ospedaliera San Gerardo(IT)
- Istituti di Ricovero e Cura a Carattere Scientifico(IT)
- Ng Teng Fong General Hospital(SG)
- University Hospital Southampton NHS Foundation Trust(GB)
- Symbiosis International University(IN)
- Libera Università Maria SS. Assunta(IT)
- Ospedale Generale Regionale Francesco Miulli(IT)
- Centre National de la Recherche Scientifique(FR)
- ParisTech(FR)
- Arts et Métiers(FR)
- Sorbonne Université(FR)
- Hôpital Tenon(FR)
- Marmara University(TR)
- Bir Hospital(NP)
- National Academy of Medical Sciences(NP)
- Muljibhai Patel Urological Hospital(IN)
- Mulawarman University(ID)
- Centre Hospitalier Universitaire Mohammed VI(MA)
- King Abdullah International Medical Research Center(SA)
- Manipal Academy of Higher Education(IN)