%0 Journal Article
%T Application of AI in urolithiasis risk of infection: a scoping review.
%A Campobasso D
%A Panizzi M
%A Bellini V
%A Ferretti S
%A Amparore D
%A Castellani D
%A Fiori C
%A Puliatti S
%A Pietropaolo A
%A Somani BK
%A Micali S
%A Porpiglia F
%A Maestroni UV
%A Bignami EG
%J Minerva Urol Nephrol
%V 76
%N 3
%D 2024 Jun
%M 38920010
暂无%R 10.23736/S2724-6051.24.05686-6
%X <B>BACKGROUND: </B>Artificial intelligence and machine learning are the new frontier in urology; they can assist the diagnostic work-up and in prognostication bring superior to the existing nomograms. Infectious events and in particular the septic risk, are one of the most common and in some cases life threatening complication in patients with urolithiasis. We performed a scoping review to provide an overview of the current application of AI in prediction the infectious complications in patients affected by urolithiasis.<BR><B>METHODS: </B>A systematic scoping review of the literature was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses for Scoping Reviews (PRISMA-ScR) guidelines by screening Medline, PubMed, and Embase to detect pertinent studies.<BR><B>RESULTS: </B>A total of 467 articles were found, of which nine met the inclusion criteria and were considered. All studies are retrospective and published between 2021 and 2023. Only two studies performed an external validation of the described models. The main event considered is urosepsis in four articles, urinary tract infection in two articles and diagnosis of infection stones in three articles. Different AI models were trained, each of which exploited several types and numbers of variables. All studies reveal good performance. Random forest and artificial neural networks seem to have higher AUC, specificity and sensibility and perform better than the traditional statistical analysis.<BR><B>CONCLUSIONS: </B>Further prospective and multi-institutional studies with external validation are needed to better clarify which variables and AI models should be integrated in our clinical practice to predict infectious events.