Evidence map›Paper›PMID 41165843›Full record

SynthesisWorld journal of urology2025

Intraoperative use of artificial intelligence (AI) during endoscopic lithotripsy: a systematic review from EAU endourology.

Andrey Morozov, Igor Matkovskiy, Bhaskar Somani, Jack Baniel, Olivier Traxer, David Lifshitz, Stanislav Ali, Yaron Erlich, Shay Golan, Dmitry Pushkar and 3 more

Abstract readSystematic Review
PubMed Publisher
In one paragraph

Synthesis in World journal of urology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Andrey MorozovInstitute for Urology and Reproductive Health, Sechenov University, Moscow, Russia.
Igor MatkovskiyN. V. Sklifosovskiy Institute of Clinical Medicine, Sechenov University, Moscow, Russia.
Bhaskar SomaniDepartment of Urology, University Hospital Southampton NHS Trust, Southampton, UK.
Jack BanielRabin Medical Center, Petah Tikva, Israel.
Olivier TraxerHôpitaux Universitaires Paris-Est, AP-HP, Université Pierre et Marie Curie Paris, Paris, France.
David LifshitzRabin Medical Center, Petah Tikva, Israel.
Stanislav AliInstitute for Urology and Reproductive Health, Sechenov University, Moscow, Russia.
Yaron ErlichRabin Medical Center, Petah Tikva, Israel.
Shay GolanRabin Medical Center, Petah Tikva, Israel.
Dmitry PushkarUrology Department, Moscow State University of Medicine and Dentistry, Moscow, Russia.
Giovanni E CacciamaniUSC Institute of Urology and Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, Los Angeles, USA.
Vineet GauharNg Teng Fong General Hospital, NUH, Singapore, Singapore.
Dmitry EnikeevInstitute for Urology and Reproductive Health, Sechenov University, Moscow, Russia. dvenikeev@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe current systematic review aims to summarize the existing data on intraoperative use of artificial intelligence (AI) during endoscopic lithotripsy in order to assess which particular applications are feasible and have prospects of wide implementation into practice. MATERIALS AND

methodsThe review included studies where adult patients with urolithiasis underwent any type of endoscopic lithotripsy with intraoperative application of AI. Preclinical trials (on animals or phantom kidneys) focusing on modelling endoscopic lithotripsy and AI application for this procedure were also considered.

resultsSix articles were included. The primary AI applications can be categorized into three domains: intraoperative navigation; tissue and stone differentiation; stone classification according to chemical composition. AI enabled reconstruction of the 3D map of endoscope movement with an accuracy of 0.6 mm and stone size measurement with an accuracy of 0.06 mm, differentiating between laser interactions with stone and tissue and differentiating between 4 common stone types (calcium oxalate monohydrate, calcium oxalate dihydrate, uric acid, and brushite). However, most of the data was obtained in experimental setups, rendering AI performance in real clinical settings still unclear.

conclusionThis review found that AI technologies show promise in endoscopic lithotripsy, with current systems already capable of performing accurate tissue and stone segmentation, intraoperative navigation, and stone classification, although at the moment still there is no solid clinical background, and our conclusions are predominantly based on experimental studies. Currently AI clinical utility is still questionable due to lack of studies, especially validated ones. Continued development and clinical adoption are needed to further improve urological surgery outcomes.

Indexed as

Artificial IntelligenceEndoscopyLithotripsyUreteroscopyUrolithiasisHumansIntraoperative CareKidney CalculiArtificial intelligenceEndoscopic lithotripsyEndourologyLithotripsyPercutaneous nephrolithotripsyRetrograde intrarenal surgeryUrolithiasis

Identifiers

PMID41165843

What OpenQuestion holds

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Registered trials

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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.