Previous systematic reviews related to machine learning (ML) in urology often overlooked the literature related to endourology. Therefore, we aim to conduct a more focused systematic review examining the use of ML algorithms for benign prostatic hyperplasia (BPH) or urolithiasis.
In addition, we are the first group to evaluate these articles using the STREAM-URO framework.
Searches of MEDLINE, Embase, and the Cochrane CENTRAL databases were conducted from inception through July 12, 2021. Keywords included those related to ML, endourology, urolithiasis, and BPH. Two reviewers screened the citations that were eligible for title, abstract and full-text screening, with conflicts resolved by a third reviewer. Two reviewers extracted information from the studies, with discrepancies resolved by a third reviewer. The data collected was then qualitatively synthesized by consensus. Two reviewers evaluated each article according to the STREAM-URO checklist with discrepancies resolved by a third reviewer.
After identifying 459 unique citations, 63 articles were retained for data extraction. Most articles consisted of tabular (n=32) and computer vision (n=23) tasks. The two most common problem types were classification (n=40) and regression (n=12). In general, most studies utilized neural networks as their ML algorithm (n=36). Among the 63 studies retrieved, 58 were related to urolithiasis and five focused on BPH. The urolithiasis studies were designed for outcome prediction (n=20), stone classification (n=18), diagnostics (n=17), and therapeutics (n=3). The BPH studies were designed for outcome prediction (n=2), diagnostics (n=2), and therapeutics (n=1). On average, the urolithiasis and BPH articles met 13.8 (SD 2.6), and 13.4 (4.1) of the 26 STREAM-URO framework criteria, respectively.
The majority of the retrieved studies successfully helped with outcome prediction, diagnostics, and therapeutics for both urolithiasis and BPH. While ML shows great promise in improving patient care, it is important to adhere to the recently developed STREAM-URO framework to ensure the development of high-quality ML studies.
Journal of endourology. 2022 Oct 20 [Epub ahead of print]
David Bouhadana, Xing Han Lu, Jack W Luo, Anis Assad, Claudia Deyirmendjian, Abbas Guennoun, David-Dan Nguyen, Jethro C C Kwong, Bilal Chughtai, Dean Elterman, Kevin Christopher Zorn, Quoc-Dien Trinh, Naeem Bhojani
McGill University Faculty of Medicine and Health Sciences, 12367, 3605 de la Montagne, Montreal, Quebec, Canada, H3G 2M1; ., McGill University School of Computer Science, 348406, Montreal, Quebec, Canada; ., McGill University Faculty of Medicine and Health Sciences, 12367, Montreal, Quebec, Canada; ., University of Montreal Hospital Centre, 25443, Urology, Montreal, Quebec, Canada; ., Université de Montréal, 5622, Medicine, Montreal, Quebec, Canada; ., University of Montreal Hospital Centre, 25443, Urology, Montreal, Quebec, Canada; ., University of Toronto, 7938, Urology, Toronto, Ontario, Canada; ., University of Toronto, 7938, Urology, Toronto, Ontario, Canada; ., Weill Cornell Medical Center, Urology, New York, New York, United States; ., University of Toronto, 7938, Urology, Toronto, Ontario, Canada; ., University of Montreal Hospital Centre, 25443, Urology, Montreal, Quebec, Canada; ., Brigham and Women's Hospital, Urology, Boston, Massachusetts, United States; ., University of Montreal Hospital Centre, 25443, Urology, Montreal, Quebec, Canada; .
PubMed http://www.ncbi.nlm.nih.gov/pubmed/36266993