The Ascent of Artificial Intelligence in Endourology: a Systematic Review Over the Last 2 Decades

Hameed, Zeeshan BM and Shah, Milap and Naik, Nithesh and Rai, Bhavan Prasad and Karimi, Hadis and Rice, Patrick and Kronenberg, Peter and Somani, Bhaskar K (2021) The Ascent of Artificial Intelligence in Endourology: a Systematic Review Over the Last 2 Decades. Current Urology Reports. pp. 22-53. ISSN 1527-2737

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Purpose of Review To highlight and review the application of artifcial intelligence (AI) in kidney stone disease (KSD) for diagnostics, predicting procedural outcomes, stone passage, and recurrence rates. The systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) checklist. Recent Findings This review discusses the newer advancements in AI-driven management strategies, which holds great promise to provide an essential step for personalized patient care and improved decision making. AI has been used in all areas of KSD including diagnosis, for predicting treatment suitability and success, basic science, quality of life (QOL), and recurrence of stone disease. However, it is still a research-based tool and is not used universally in clinical practice. This could be due to a lack of data infrastructure needed to train the algorithms, wider applicability in all groups of patients, complexity of its use and cost involved with it. Summary The constantly evolving literature and future research should focus more on QOL and the cost of KSD treatment and develop evidence-based AI algorithms that can be used universally, to guide urologists in the management of stone disease.

Item Type: Article
Uncontrolled Keywords: Machine learning · Artificial intelligence · Endourology · PCNL · Ureteroscopy · ESWL
Subjects: Engineering > MIT Manipal > Mechanical and Manufacturing
Medicine > KMC Manipal > Urology
Depositing User: MIT Library
Date Deposited: 26 Mar 2022 08:41
Last Modified: 26 Mar 2022 08:41

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