Bob Yang1, Domenico Veneziano2, Bhaskar K Somani3. 1. Royal Hampshire Hospital, Winchester, UK. 2. Department of Urology and Kidney Transplant, GOM, Reggio Calabria, Italy. 3. University Hospital Southampton NHS Trust, UK.
Abstract
PURPOSE OF REVIEW: There has a been rapid progress in the use of artificial intelligence in all aspects of healthcare, and in urology, this is particularly astute in the overall management of urolithiasis. This article reviews advances in the use of artificial intelligence for the diagnosis, treatment and prevention of urinary stone disease over the last 2 years. Pertinent studies were identified via a nonsystematic review of the literature performed using MEDLINE and the Cochrane database. RECENT FINDINGS: Twelve articles have been published, which met the inclusion criteria. This included three articles in the detection and diagnosis of stones, six in the prediction of postprocedural outcomes including percutaneous nephrolithotomy and shock wave lithotripsy, and three in the use of artificial intelligence in prevention of stone disease by predicting patients at risk of stones, detecting the stone type via digital photographs and detecting risk factors in patients most at risk of not attending outpatient appointments. SUMMARY: Our knowledge of artificial intelligence in urology has greatly advanced in the last 2 years. Its role currently is to aid the endourologist as opposed to replacing them. However, the ability of artificial intelligence to efficiently process vast quantities of data, in combination with the shift towards electronic patient records provides increasingly more 'big data' sets. This will allow artificial intelligence to analyse and detect novel diagnostic and treatment patterns in the future.
PURPOSE OF REVIEW: There has a been rapid progress in the use of artificial intelligence in all aspects of healthcare, and in urology, this is particularly astute in the overall management of urolithiasis. This article reviews advances in the use of artificial intelligence for the diagnosis, treatment and prevention of urinary stone disease over the last 2 years. Pertinent studies were identified via a nonsystematic review of the literature performed using MEDLINE and the Cochrane database. RECENT FINDINGS: Twelve articles have been published, which met the inclusion criteria. This included three articles in the detection and diagnosis of stones, six in the prediction of postprocedural outcomes including percutaneous nephrolithotomy and shock wave lithotripsy, and three in the use of artificial intelligence in prevention of stone disease by predicting patients at risk of stones, detecting the stone type via digital photographs and detecting risk factors in patients most at risk of not attending outpatient appointments. SUMMARY: Our knowledge of artificial intelligence in urology has greatly advanced in the last 2 years. Its role currently is to aid the endourologist as opposed to replacing them. However, the ability of artificial intelligence to efficiently process vast quantities of data, in combination with the shift towards electronic patient records provides increasingly more 'big data' sets. This will allow artificial intelligence to analyse and detect novel diagnostic and treatment patterns in the future.
Authors: E Checcucci; D Amparore; G Volpi; F Piramide; S De Cillis; A Piana; P Alessio; P Verri; S Piscitello; B Carbonaro; J Meziere; D Zamengo; A Tsaturyan; G Cacciamani; Juan Gomez Rivas; S De Luca; M Manfredi; C Fiori; E Liatsikos; F Porpiglia Journal: World J Urol Date: 2021-09-01 Impact factor: 3.661
Authors: Ee Jean Lim; Daniele Castellani; Wei Zheng So; Khi Yung Fong; Jing Qiu Li; Ho Yee Tiong; Nariman Gadzhiev; Chin Tiong Heng; Jeremy Yuen-Chun Teoh; Nithesh Naik; Khurshid Ghani; Kemal Sarica; Jean De La Rosette; Bhaskar Somani; Vineet Gauhar Journal: J Clin Med Date: 2022-08-31 Impact factor: 4.964