Jian Chen1, Daphne Remulla1, Jessica H Nguyen1, D Aastha2, Yan Liu2, Prokar Dasgupta3, Andrew J Hung1. 1. Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation & Education, University of Southern California Institute of Urology, Los Angeles, CA, USA. 2. Computer Science Department, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA. 3. Division of Transplantation Immunology and Mucosal Biology, Faculty of Life Sciences and Medicine, Kings College London, London, UK.
Abstract
OBJECTIVE: To investigate the applications of artificial intelligence (AI) in diagnosis, treatment and outcome predictionin urologic diseases and evaluate its advantages over traditional models and methods. MATERIALS AND METHODS: A literature search was performed after PROSPERO registration (CRD42018103701) and in compliance with Preferred Reported Items for Systematic Reviews and Meta-Analyses (PRISMA) methods. Articles between 1994 and 2018 using the search terms "urology", "artificial intelligence", "machine learning" were included and categorized by the application of AI in urology. Review articles, editorial comments, articles with no full-text access, and nonurologic studies were excluded. RESULTS: Initial search yielded 231 articles, but after excluding duplicates and following full-text review and examination of article references, only 111 articles were included in the final analysis. AI applications in urology include: utilizing radiomic imaging or ultrasonic echo data to improve or automate cancer detection or outcome prediction, utilizing digitized tissue specimen images to automate detection of cancer on pathology slides, and combining patient clinical data, biomarkers, or gene expression to assist disease diagnosis or outcome prediction. Some studies employed AI to plan brachytherapy and radiation treatments while others used video based or robotic automated performance metrics to objectively evaluate surgical skill. Compared to conventional statistical analysis, 71.8% of studies concluded that AI is superior in diagnosis and outcome prediction. CONCLUSION: AI has been widely adopted in urology. Compared to conventional statistics AI approaches are more accurate in prediction and more explorative for analyzing large data cohorts. With an increasing library of patient data accessible to clinicians, AI may help facilitate evidence-based and individualized patient care.
OBJECTIVE: To investigate the applications of artificial intelligence (AI) in diagnosis, treatment and outcome predictionin urologic diseases and evaluate its advantages over traditional models and methods. MATERIALS AND METHODS: A literature search was performed after PROSPERO registration (CRD42018103701) and in compliance with Preferred Reported Items for Systematic Reviews and Meta-Analyses (PRISMA) methods. Articles between 1994 and 2018 using the search terms "urology", "artificial intelligence", "machine learning" were included and categorized by the application of AI in urology. Review articles, editorial comments, articles with no full-text access, and nonurologic studies were excluded. RESULTS: Initial search yielded 231 articles, but after excluding duplicates and following full-text review and examination of article references, only 111 articles were included in the final analysis. AI applications in urology include: utilizing radiomic imaging or ultrasonic echo data to improve or automate cancer detection or outcome prediction, utilizing digitized tissue specimen images to automate detection of cancer on pathology slides, and combining patient clinical data, biomarkers, or gene expression to assist disease diagnosis or outcome prediction. Some studies employed AI to plan brachytherapy and radiation treatments while others used video based or robotic automated performance metrics to objectively evaluate surgical skill. Compared to conventional statistical analysis, 71.8% of studies concluded that AI is superior in diagnosis and outcome prediction. CONCLUSION: AI has been widely adopted in urology. Compared to conventional statistics AI approaches are more accurate in prediction and more explorative for analyzing large data cohorts. With an increasing library of patient data accessible to clinicians, AI may help facilitate evidence-based and individualized patient care.
Authors: Jethro C C Kwong; Adree Khondker; Christopher Tran; Emily Evans; Adrian I Cozma; Ashkan Javidan; Amna Ali; Munir Jamal; Thomas Short; Frank Papanikolaou; John R Srigley; Benjamin Fine; Andrew Feifer Journal: Can Urol Assoc J Date: 2022-06 Impact factor: 2.052
Authors: Andrew B Chen; Taseen Haque; Sidney Roberts; Sirisha Rambhatla; Giovanni Cacciamani; Prokar Dasgupta; Andrew J Hung Journal: Urol Clin North Am Date: 2021-10-23 Impact factor: 2.766