ReviewAsian journal of urology2023
Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review.
Review in Asian journal of urology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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
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Who cites it
8 citing papers in PubMed.
- Magnetically actuated microrobots in urolithiasis: emerging frontiers in kidney stone diagnosis and management.Inflammopharmacology · 2026Review
- Tips and tricks for reducing radiation exposure during retrograde intrarenal surgery: A literature review by the European Association of Urology Section of Endourology.Asian journal of urology · 2026Review
- Construction of a LASSO regression-based predictive model for recurrence of urinary tract stones.Translational andrology and urology · 2026Article
- Applications of artificial intelligence in abdominal imaging.Abdominal radiology (New York) · 2025Review
- Development and multi-institutional validation of the Swin transformer model for prediction of spontaneous passage of ureteral calculi: A retrospective multicentric study.Urolithiasis · 2025Article
- Optimizing infusion-suction diameter ratio (ISDR) in ureteroscopy: in vitro pressure and flow prediction and management.Urolithiasis · 2025Article
- Development of a new scoring system predicting medical expulsive therapy success on 4-10 mm distal ureteral stones: medical expulsive therapy stone score (METSS).Urolithiasis · 2023Article
- Improved Detection of Urolithiasis Using High-Resolution Computed Tomography Images by a Vision Transformer Model.International neurourology journal · 2023Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To provide a comprehensive review on the existing research and evidence regarding artificial intelligence (AI) applications in the assessment and management of urinary stone disease. Methods: A comprehensive literature review was performed using PubMed, Scopus, and Google Scholar databases to identify publications about innovative concepts or supporting applications of AI in the improvement of every medical procedure relating to stone disease. The terms ''endourology'', ''artificial intelligence'', ''machine learning'', and ''urolithiasis'' were used for searching eligible reports, while review articles, articles referring to automated procedures without AI application, and editorial comments were excluded from the final set of publications. The search was conducted from January 2000 to September 2023 and included manuscripts in the English language. Results: A total of 69 studies were identified. The main subjects were related to the detection of urinary stones, the prediction of the outcome of conservative or operative management, the optimization of operative procedures, and the elucidation of the relation of urinary stone chemistry with various factors. Conclusion: AI represents a useful tool that provides urologists with numerous amenities, which explains the fact that it has gained ground in the pursuit of stone disease management perfection. The effectiveness of diagnosis and therapy can be increased by using it as an alternative or adjunct to the already existing data. However, little is known concerning the potential of this vast field. Electronic patient records, containing big data, offer AI the opportunity to develop and analyze more precise and efficient diagnostic and treatment algorithms. Nevertheless, the existing applications are not generalizable in real-life practice, and high-quality studies are needed to establish the integration of AI in the management of urinary stone disease.
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Registered trials
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