Machine learning models to predict the zero-fragment rate and lower pole access with FANS during flexible Ureteroscopy-an EAU section of endourology study.

Frédéric Panthier, Daron Smith, Olivier Traxer, Daniele Castellani, Steffi Yuen, Bhaskar Somani, Vineet Gauhar

Abstract readMulticenter Study

In one paragraph

Article 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.

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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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

The future of endourology is now.World journal of urology · 2026
Article

4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Frédéric PanthierDepartment of Urology, Westmoreland Street Hospital, and Clinical Microbiology, UCLH NHS Foundation Trust, London, UK. fredericpanthier@gmail.com.

Daron SmithDepartment of Urology, Westmoreland Street Hospital, and Clinical Microbiology, UCLH NHS Foundation Trust, London, UK.

Olivier TraxerService d'Urologie, Assistance-Publique Hôpitaux de Paris, Hôpital Tenon, Sorbonne Université, 75020, Paris, France.

Daniele CastellaniEndourology Technology Section of European Association of Urology (EAU), Arnhem, The Netherlands.

Steffi YuenEndourology Technology Section of European Association of Urology (EAU), Arnhem, The Netherlands.

Bhaskar SomaniUniversity Hospital Southampton NHS Foundation Trust, Southampton, United Kingdom.

Vineet GauharDepartment of Urology, Ng Teng Fong General Hospital, Singapore, Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionSuction devices such as flexible and navigable suction ureteral access sheath (FANS) are promising tools to reach the zero-fragment rate (ZFR) after flexible ureteroscopy (FURS) and laser lithotripsy. FANS could especially be useful for lower pole stones (LPS), avoiding postoperative retained stone dust. Using Machine Learning (ML) models, we aimed to predict the ZFR after FURS with FANS and secondarily the possible access to the lower pole (LP).

methodsData from patients who underwent FURS in 25 centers worldwide were prospectively collected (Aug 2023-Jan 2024). Exclusion criteria were abnormal renal anatomy and ureteral stones. ZFR and LP access were respectively defined as the total absence of residual fragments on computed tomography at 1-month follow-up and the ability to place the FANS into a LP calyx. After data normalization and splitting (training-test (80-20%)), Eight ML models were evaluated to predict separately ZFR and LPS, using binary classification.

resultsA total of 390 patients were included with a median age of 49 (36-61)years. The median stone volume was 1440 (1006-2219)mm

conclusionBased on preoperative data and ML, we can accurately predict the ZFR and LP access during FURS with FANS. Our models could improve the elective indications for FANS utilization.

Indexed as

Kidney CalculiMachine LearningUreteroscopyAdultFemaleHumansMaleMiddle AgedProspective StudiesUreteroscopesFlexible ureteroscopyLaserLithotripsySuctionUreteral access sheath

Identifiers

PMID40618297

DOI10.1007/s00345-025-05798-9

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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.