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Aim and objective <p>Flexible ureteroscopy (fURS) is a well-established modality for managing urolithiasis in patients with congenital renal anomalies such as horseshoe kidneys (HK), malrotated kidneys (MK), and pelvic ectopic kidneys (PEK). Still, these anatomical variants present unique challenges that complicate stone clearance and procedural planning. We aim to apply machine learning (ML) and explainable artificial intelligence (XAI) techniques to identify predictors of stone-free status (SFS) following fURS in patients with anomalous kidneys.</p> Methods <p>We retrospectively analysed adult patients with HK, MK, or EK who underwent fURS and laser lithotripsy for renal stones at a tertiary referral center. A ML model incorporating clinical and intraoperative variables was developed to predict SFS. SHAP (SHapley Additive exPlanations) values and decision tree analysis were used to interpret feature importance and model behaviour.</p> Results <p>A total of 569 cases were analysed between 2017 and 2021, with a female: male ratio of 3:1. Regarding anatomical anomalies, 50.62% had HSK, 22.67% had PEK and 26.71% had MK. Most of the patients presented with multiple (59.58%), small (76.80%) and soft stones (56.94%). MK showed the highest SFS rates, suggesting this is the most favourable anomaly for fURS. The presence of residual fragments at the end of the procedure was the strongest negative predictor of SFS, followed by longer operative time and older patient age. PEK exhibited the greatest heterogeneity in outcomes. SHAP analysis provided individualized and global insights into feature contributions.</p> Conclusion <p>Explainable AI offers a transparent and clinically meaningful approach to predicting SFS in patients with renal anomalies undergoing fURS. These insights can guide preoperative risk stratification and inform surgical strategy in a domain where standardised evidence is lacking.</p>

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Flexible ureteroscopy in renal anomalies: an explainable AI model for surgical outcome prediction from EAU endourology

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月2日

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  • Carlotta Nedbal,
  • Vineet Gauhar,
  • Sairam Adithya,
  • Nithesh Naik,
  • Shilpa Gite,
  • Het Sevalia,
  • Daniele Castellani,
  • Andrea Gregori,
  • Frédéric Panthier,
  • Yiloren Tanidir,
  • Anil Shrestha,
  • Vikram Sridharan,
  • Abhishek Singh,
  • Boyke Soebhali,
  • Mohamed Amine Lakmichi,
  • Saeed Biin Hamri,
  • Bhaskar Kumar Somani

摘要

Aim and objective

Flexible ureteroscopy (fURS) is a well-established modality for managing urolithiasis in patients with congenital renal anomalies such as horseshoe kidneys (HK), malrotated kidneys (MK), and pelvic ectopic kidneys (PEK). Still, these anatomical variants present unique challenges that complicate stone clearance and procedural planning. We aim to apply machine learning (ML) and explainable artificial intelligence (XAI) techniques to identify predictors of stone-free status (SFS) following fURS in patients with anomalous kidneys.

Methods

We retrospectively analysed adult patients with HK, MK, or EK who underwent fURS and laser lithotripsy for renal stones at a tertiary referral center. A ML model incorporating clinical and intraoperative variables was developed to predict SFS. SHAP (SHapley Additive exPlanations) values and decision tree analysis were used to interpret feature importance and model behaviour.

Results

A total of 569 cases were analysed between 2017 and 2021, with a female: male ratio of 3:1. Regarding anatomical anomalies, 50.62% had HSK, 22.67% had PEK and 26.71% had MK. Most of the patients presented with multiple (59.58%), small (76.80%) and soft stones (56.94%). MK showed the highest SFS rates, suggesting this is the most favourable anomaly for fURS. The presence of residual fragments at the end of the procedure was the strongest negative predictor of SFS, followed by longer operative time and older patient age. PEK exhibited the greatest heterogeneity in outcomes. SHAP analysis provided individualized and global insights into feature contributions.

Conclusion

Explainable AI offers a transparent and clinically meaningful approach to predicting SFS in patients with renal anomalies undergoing fURS. These insights can guide preoperative risk stratification and inform surgical strategy in a domain where standardised evidence is lacking.