Evidence map›Paper›PMID 39956703›Full record

ArticleJournal of pediatric urology2025

A living scoping review and online repository of artificial intelligence models in pediatric urology: Results from the AI-PEDURO collaborative.

Adree Khondker, Jethro Cc Kwong, Ihtisham Ahmad, Zwetlana Rajesh, Rahim Dhalla, Wyatt MacNevin, Mandy Rickard, Lauren Erdman, Andrew T Gabrielson, David-Dan Nguyen and 12 more

Abstract readScoping Review
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of pediatric urology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
–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

10 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Reporting of race and ethnicity in studies of artificial intelligence in pediatric urology A secondary analysis of the AI-PEDURO online repository.Canadian Urological Association journal = Journal de l'Association des urologues du Canada · 2026
    Review
  6. Article
  7. Article
  8. Article
  9. Review
  10. Evaluation of ChatGPT's performance on answering pediatric urology questions based on association guidelines.Canadian Urological Association journal = Journal de l'Association des urologues du Canada · 2025
    Article
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

22 authors.

Adree KhondkerDivision of Urology, The Hospital for Sick Children, Toronto, ON, Canada; Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada. Electronic address: adree.khondker@mail.utoronto.ca.
Jethro Cc KwongDivision of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada; Temerty School of Medicine, University of Toronto, Toronto, ON, Canada.
Ihtisham AhmadTemerty School of Medicine, University of Toronto, Toronto, ON, Canada.
Zwetlana RajeshTemerty School of Medicine, University of Toronto, Toronto, ON, Canada.
Rahim DhallaSchulich School of Medicine, University of Western Ontario, London, ON, Canada.
Wyatt MacNevinDepartment of Urology, IWK Hospital, Halifax, NS, Canada.
Mandy RickardDivision of Urology, The Hospital for Sick Children, Toronto, ON, Canada.
Lauren ErdmanTemerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, ON, Canada; James M. Anderson Center for Health Systems Excellence, Cincinnati Children's Hospital Medical Center and University of Cincinnati School of Medicine, Cincinnati, OH, USA.
Andrew T GabrielsonJames Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
David-Dan NguyenDivision of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
Jin Kyu KimDivision of Urology, The Hospital for Sick Children, Toronto, ON, Canada; Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Urology, Riley Children's Hospital, Indianapolis, IN, USA.
Tariq AbbasDivision of Urology, Sidra Medicine, Doha, Qatar.
Nicolas FernandezDivision of Pediatric Urology, Seattle Children's Hospital, Seattle, WA, USA.
Katherine FischerDivision of Urology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Lisette A T HoenDepartment of Urology, Sophia Children's Hospital, Erasmus University Medical Center, Rotterdam, the Netherlands.
Daniel T KeefeDepartment of Urology, IWK Hospital, Halifax, NS, Canada.
Caleb P NelsonDepartment of Urology, Boston Children's Hospital, Boston, MA, USA.
Bernarda ViteriDivision of Nephrology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Hsin-Hsiao Scott WangDepartment of Urology, Boston Children's Hospital, Boston, MA, USA.
John WeaverDepartment of Urology, Cleveland Clinic Children's, Cleveland, OH, USA.
Priyank YadavDivision of Urology, Sanjay Gandhi Institute of Medical Sciences, Lucknow, India.
Armando J LorenzoDivision of Urology, The Hospital for Sick Children, Toronto, ON, Canada; Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada. Electronic address: armando.lorenzo@sickkids.ca.

Funding

IMPAKT Study: Imaging Modalities in Pediatric Assessments of Kidney TransplantsK23DK131331 · NIDDK · CHILDREN'S HOSP OF PHILADELPHIA · PI Bernarda Viteri Baquerizo · 2022 to 2026
$899k
NIDDK NIH HHS K23 DK131331
6 · The paper itself

Abstract

introductionArtificial intelligence (AI) is increasingly being applied across pediatric urology. We provide a living scoping review and online repository developed by the AI in PEDiatric UROlogy (AI-PEDURO) collaborative that summarizes the current and emerging evidence on the AI models developed in pediatric urology. MATERIAL AND

methodsThe protocol was published a priori, and Preferred Reporting Items for Systematic Review and Meta-analysis Scoping Review (PRISMA-ScR) guidelines were followed. We conducted a comprehensive search of four electronic databases and reviewed relevant data sources from inception until June 2024 to identify studies that have implemented AI for prediction, classification, or risk stratification for pediatric urology conditions. Model quality was assessed by the APPRAISE-AI tool.

resultsOverall, 59 studies were included in this review from 1557 unique records. Of the 59 published studies, 44 studies (75 %) were published after 2019, with hydronephrosis and vesicoureteral reflux/urinary tract infection as the most common topics (17 studies, 28 % each). Studies originated from USA (22 studies, 37 %), Canada (10 studies, 17 %), China (8 studies, 14 %), and Turkey (7 studies, 12 %). Neural network (35 studies, 59 %), support-vector-machine (21 studies, 36 %), and tree-based models (19 studies, 32 %) were the most used machine learning algorithms, with 14 studies (24 %) providing useable repositories or applications. APPRAISE-AI assessed 12 studies (20 %) of studies as low quality, 39 studies (66 %) as moderate quality, and 8 studies (14 %) as high quality, with specific improvements noted in model robustness and reporting standards over time (p = 0.03). Findings were synthesized into an online repository (www.aipeduro.com). DISCUSSION: There is an increasing pace of AI model development in pediatric urology. Model topics are broad, algorithm choice is diverse, and the overall quality of models are improving over time. While there is still a lack of clinical translation of the AI models in pediatric urology, the usage of online repositories and reporting frameworks can facilitate sharing, improvement, and clinical implementation of future models.

conclusionsThis living scoping review and online repository will highlight the current landscape of AI models in pediatric urology and facilitate their clinical translation and inform future research initiatives. From this work, we provide a summary of recommendations based on the current literature for future studies.

Indexed as

Artificial IntelligencePediatricsUrologic DiseasesUrologyChildHumansArtificial intelligenceMachine learningModel quality assessmentPediatric urologyPrediction

Identifiers

PMID39956703
PMCPMC12908176

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.