ArticleNPJ digital medicine2025
Clinical validation of an AI-assisted system for real-time kidney stone detection during flexible ureteroscopic surgery.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Study on automatic recognition of stone composition in intraoperative endoscopic images-a single center study.The Canadian journal of urology · 2026Article
- A validated custom pipeline for three-dimensional kidney stone renderings tocreate an open access repository.Urolithiasis · 2026Article
- Quantifying ergonomic challenges for urologists operating flexible ureteroscopes through artificial intelligence-based posture estimation.World journal of urology · 2026Article
- Endoscopic combined intrarenal surgery: From percutaneous nephrolithotomy to the future of stone management.Investigative and clinical urology · 2026Review
- Ureteral Orifice Detection in Ureteroscopic Images Based on Large-Kernel Convolutional Neural Networks and Attention-Based Feature Fusion.Bioengineering (Basel, Switzerland) · 2026Article
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
Flexible ureteroscopy (FURS) is a minimally invasive, standard treatment for kidney stones. This study presents the development and clinical validation of an artificial intelligence system during FURS (AiFURS) for real-time detection, classification, and measurement of stones. Using 6170 annotated ureteroscopy video frames representing 11,870 labeled stones, the AiFURS was trained to identify stone type, size, and number. Ex vivo validation across 191 groups predicted stone counts precisely (r > 0.9) in 300 samples. Size predictions for stones >2 mm (n = 100, r = 0.81) correlated with gold-standard caliper measurements. In vivo and external validation of 100 and 80 cases, respectively, demonstrated diagnostic accuracy (92.2-95.3% and 86.8-92.2%, respectively) for patient-level stone type prediction, outperforming expert surgeons. Logistic regression further identified the proportion of residual fragments (RFs) > 2 mm, measured during the final minutes of FURS, as an independent predictor of reoperation. AiFURS offers a novel solution to enhance surgical accuracy, reduce complications, and improve outcomes in endourology.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.