ArticleUrology2022
Automated Machine Learning Segmentation and Measurement of Urinary Stones on CT Scan.
Article in Urology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in urolithiasis: a systematic review of utilization and effectiveness.World journal of urology · 2024Pooled it
- Prevention of urinary stones with hydration: a randomised clinical trial of an adherence intervention.Lancet (London, England) · 2026Trial
- Validation of an Automated CT Image Analysis in the Prevention of Urinary Stones with Hydration Trial.Journal of endourology · 2025Trial
- Accuracy and precision of automated kidney stone detection on CT.Abdominal radiology (New York) · 2026Article
- Clinical validation of an AI-assisted system for real-time kidney stone detection during flexible ureteroscopic surgery.NPJ digital medicine · 2025Article
- Review
- A roadmap of artificial intelligence applications in pediatric surgery: a comprehensive review of applications, challenges, and ethical considerations.Pediatric surgery international · 2025Review
- Artificial Intelligence Tools in Pediatric Urology: A Comprehensive Review of Recent Advances.Diagnostics (Basel, Switzerland) · 2024Review
- Comparison of automated kidney stone size measurement and volumetry in photon counting CT compared to 3rd generation dual energy CT and physically measurements - an ex vivo study.World journal of urology · 2024Article
- A machine learning approach using stone volume to predict stone-free status at ureteroscopy.World journal of urology · 2024Article
- Article
- Clinical Reproducibility of the Stone Volume Measurement: A "Kidney Stone Calculator" Study.Journal of clinical medicine · 2023Article
- Theranostic roles of machine learning in clinical management of kidney stone disease.Computational and structural biotechnology journal · 2023Review
- Current research and future directions in non-malignant urologic research - proceedings of the annual CAIRIBU meeting.American journal of clinical and experimental urology · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectivesTo evaluate the performance of an engineered machine learning algorithm to identify kidney stones and measure stone characteristics without the need for human input.
methodsWe performed a cross-sectional study of 94 children and adults who had kidney stones identified on non-contrast CT. A previously developed deep learning algorithm was trained to segment renal anatomy and kidney stones and to measure stone features. The performance and speed of the algorithm to measure renal anatomy and kidney stone features were compared to the current gold standard of human measurement performed by 3 independent reviewers.
resultsThe algorithm was 100% sensitive and 100% specific in detecting individual kidney stones. The mean stone volume segmented by the algorithm was smaller than that of human reviewers and had moderate overlap (Dice score: 0.66). There was substantial variation between human reviewers in total segmented stone volume (Jaccard score: 0.17) and volume of the single largest stone (Jaccard score: 0.33). Stone segmentations performed by the machine learning algorithm more precisely approximated stone borders than those performed by human reviewers on qualitative assessment.
conclusionAn engineered machine learning algorithm can identify and characterize stones more accurately and reliably than humans, which has the potential to improve the precision and efficiency of assessing kidney stone burden.
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Registered trials
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