AI-PEDURO - Artificial intelligence in pediatric urology: Protocol for a living scoping review and online repository

Adree Khondker; Jethro C C Kwong; Mandy Rickard; Lauren Erdman; Andrew T Gabrielson; David-Dan Nguyen; Jin Kyu Kim; Tariq Abbas; Nicolas Fernandez; Katherine Fischer; Lisette A 't Hoen; Daniel T Keefe; Caleb P Nelson; Bernarda Viteri; Hsin-Hsiao Scott Wang; John Weaver; Priyank Yadav; Armando J Lorenzo

doi:10.1016/j.jpurol.2024.10.003

AI-PEDURO - Artificial intelligence in pediatric urology: Protocol for a living scoping review and online repository

J Pediatr Urol. 2024 Oct 5:S1477-5131(24)00523-0. doi: 10.1016/j.jpurol.2024.10.003. Online ahead of print.

Authors

Adree Khondker¹, Jethro C C Kwong², Mandy Rickard³, Lauren Erdman⁴, Andrew T Gabrielson⁵, David-Dan Nguyen⁶, Jin Kyu Kim⁷, Tariq Abbas⁸, Nicolas Fernandez⁹, Katherine Fischer¹⁰, Lisette A 't Hoen¹¹, Daniel T Keefe¹², Caleb P Nelson¹³, Bernarda Viteri¹⁴, Hsin-Hsiao Scott Wang¹³, John Weaver¹⁵, Priyank Yadav¹⁶, Armando J Lorenzo¹⁷

Affiliations

¹ Division 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.
² Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada; Temerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, ON, Canada.
³ Division of Urology, The Hospital for Sick Children, Toronto, ON, Canada.
⁴ James M. Anderson Center for Health Systems Excellence, Cincinnati Children's Hospital Medical Center and University of Cincinnati School of Medicine, Cincinnati, OH, USA.
⁵ James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
⁶ Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada.
⁷ Division 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.
⁸ Division of Urology, Sidra Medicine, Doha, Qatar.
⁹ Division of Pediatric Urology, Seattle Children's Hospital, Seattle, WA, USA.
¹⁰ Division of Urology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
¹¹ Department of Urology, Erasmus University Medical Center, Rotterdam, the Netherlands.
¹² Department of Urology, IWK Hospital, Halifax, NS, Canada.
¹³ Department of Urology, Boston Children's Hospital, Boston, MA, USA.
¹⁴ Division of Nephrology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
¹⁵ Department of Urology, Cleveland Clinic Children's, Cleveland, OH, USA.
¹⁶ Division of Urology, Sanjay Gandhi Institute of Medical Sciences, Lucknow, India.
¹⁷ Division 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.

PMID: 39424499
DOI: 10.1016/j.jpurol.2024.10.003

Abstract

Background: Artificial intelligence (AI) and machine learning (ML) methods are increasingly being applied in pediatric urology across a growing number of settings, with more extensive databases and wider interest for use in clinical practice. More than 30 ML models have been published in the pediatric urology literature, but many lack items required by contemporary reporting frameworks to be high quality. For example, most studies lack multi-institution validation, validation over time, and validation within the clinical environment, resulting in a large discrepancy between the number of models developed versus the number of models deployed in a clinical setting, a phenomenon known as the AI chasm. Furthermore, pediatric urology is a unique subspecialty of urology with low frequency conditions and complex phenotypes where clinical management can rely on a lower quality of evidence.

Objective: To establish the AI in PEDiatric UROlogy (AI-PEDURO) collaborative, which will carry out a living scoping review and create an online repository (www.aipeduro.com) for models in the field and facilitate an evidence synthesis of AI models in pediatric urology.

Methods and analysis: The scoping review will follow PRISMA-ScR guidelines. We will include ML models identified through standardized search methods of four databases, hand-search papers, and user-submitted models. Retrieved records will be included if they involve ML algorithms for prediction, classification, or risk stratification for pediatric urology conditions. The results will be tabulated and assessed for trends within the literature. Based on data availability, models will be divided into clinical disease sections (e.g. hydronephrosis, hypospadias, vesicoureteral reflux). A risk assessment will be performed using the APPRAISE-AI tool. The retrieved model cards (brief summary model characteristics in table form) will be uploaded to the online repository for open access to clinicians, patients, and data scientists, and will be linked to the Digital Object Identifier (DOI) for each article.

Discussion and conclusion: We hope this living scoping review and online repository will offer a valuable reference for pediatric urologists to assess disease-specific ML models' scope, validity, and credibility to encourage opportunities for collaboration, external validation, clinical testing, and responsible deployment. In addition, the repository may aid in identifying areas in need of further research.

Keywords: Artificial Intelligence; Machine learning; Online repository; Scoping review.