ReviewCureus2025
Artificial Intelligence in Urolithiasis Imaging and Intervention: A Narrative Review of Current Applications, Barriers, and Future Directions.
Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The treatment of urolithiasis is changing quickly by moving away from conventional diagnostic techniques and toward more complex, data-driven strategies. A major part in this change is being played by artificial intelligence (AI) through providing the clinicians with invaluable assistance. This study examines the state of AI applications in urolithiasis today and how they affect everything from treatment planning to initial imaging. AI models are improving the accuracy of computed tomography (CT) and ultrasonography (US) in diagnoses. These techniques provide automatic stone detection throughout the diagnostic process and a precise stone burden calculation, and even assistance in differentiating difficult mimics, such as ureteral stones, from phleboliths. Additionally, sophisticated algorithms and radiomics are demonstrating great promise in determining the composition of stones preoperatively from imaging data or even digital photos. AI has also changed and improved the intervention for kidney stones, which is highlighted by models now capable of predicting the success of procedures like extracorporeal shock wave lithotripsy (ESWL) and percutaneous nephrolithotomy (PCNL), in some cases outperforming traditional scoring systems. Despite this progress, significant hurdles remain, particularly the need for large datasets and ensuring models are reliable and generalizable across different clinical settings. Successfully integrating these powerful tools into daily urological practice will require a concerted effort toward developing best-practice guidelines, robust training programs, and strong interdisciplinary collaboration. This review aims to summarize current AI applications in imaging and intervention for urolithiasis, identify limitations, and outline future research directions.
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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.