SWL literature
SWL Literature

Ganesan V. et al., 2021: Artificial intelligence in stone disease.

Ganesan V, Pearle MS.
UT Southwestern Medical Center.
Professor of Urology and Internal Medicine, Charles and Jane Pak Center for Mineral Metabolism, UT Southwestern Medical Center, Dallas, TX, USA.

Abstract

Purpose of review: Artificial intelligence (AI) is the ability of a machine, or computer, to simulate intelligent behavior. In medicine, the use of large datasets enables a computer to learn how to perform cognitive tasks, thereby facilitating medical decision-making. This review aims to describe advancements in AI in stone disease to improve diagnostic accuracy in determining stone composition, to predict outcomes of surgical procedures or watchful waiting and ultimately to optimize treatment choices for patients.

Recent findings: AI algorithms show high accuracy in different realms including stone detection and in the prediction of surgical outcomes. There are machine learning algorithms for outcomes after percutaneous nephrolithotomy, extracorporeal shockwave lithotripsy, and for ureteral stone passage. Some of these algorithms show better predictive capabilities compared to existing scoring systems and nomograms.

Summary: The use of AI can facilitate the development of diagnostic and treatment algorithms in patients with stone disease. Although the generalizability and external validity of these algorithms remain uncertain, the development of highly accurate AI-based tools may enable the urologist to provide more customized patient care and superior outcomes.
Curr Opin Urol. 2021 Jul 1;31(4):391-396. doi: 10.1097/MOU.0000000000000896. PMID: 33965985.

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Comments 1

Peter Alken on Thursday, October 21 2021 08:30

Here the reader has got them all. Unfortunately it is not known how wide-spread the application rates of these AI algorithms are outside the departments where they were developed. In addition there are rarely follow-up reports of the same authors showing continues evaluation and adaptation. I am only aware of one such group (1,2), however, their work deals with percutaneous nephrolithotomy.

1. Aminsharifi A, Irani D, Pooyesh S, et al. Artificial neural network system to predict the postoperative outcome of percutaneous nephrolithotomy. J Endourol 2017; 31:461–467.
2. Aminsharifi A, Irani D, Tayebi S, et al. Predicting the postoperative outcome of percutaneous nephrolithotomy with machine learning system: software validation and comparative analysis with guy's stone score and the CROES Nomogram. J Endourol 2020; 34:692–699.

See also:
Rice P, Pugh M, Geraghty R, Hameed BZ, Shah M, Somani BK
Machine Learning Models for Predicting Stone-Free Status after Shockwave Lithotripsy: A Systematic Review and Meta-Analysis.
.Urology. 2021 Apr 21:S0090-4295(21)00331-9. doi: 10.1016/j.urology.2021.04.006. Online ahead of print.PMID: 33894229

Peter Alken

Here the reader has got them all. Unfortunately it is not known how wide-spread the application rates of these AI algorithms are outside the departments where they were developed. In addition there are rarely follow-up reports of the same authors showing continues evaluation and adaptation. I am only aware of one such group (1,2), however, their work deals with percutaneous nephrolithotomy. 1. Aminsharifi A, Irani D, Pooyesh S, et al. Artificial neural network system to predict the postoperative outcome of percutaneous nephrolithotomy. J Endourol 2017; 31:461–467. 2. Aminsharifi A, Irani D, Tayebi S, et al. Predicting the postoperative outcome of percutaneous nephrolithotomy with machine learning system: software validation and comparative analysis with guy's stone score and the CROES Nomogram. J Endourol 2020; 34:692–699. See also: Rice P, Pugh M, Geraghty R, Hameed BZ, Shah M, Somani BK Machine Learning Models for Predicting Stone-Free Status after Shockwave Lithotripsy: A Systematic Review and Meta-Analysis. .Urology. 2021 Apr 21:S0090-4295(21)00331-9. doi: 10.1016/j.urology.2021.04.006. Online ahead of print.PMID: 33894229 Peter Alken
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