How the use of the artificial intelligence could improve surgical skills in urology: state of the art and future perspectives. Issue 4 (July 2021)

• Record Type: Journal Article
• Authors: Cacciamani, Giovanni E.; Anvar, Arya; Chen, Andrew; Gill, Inderbir; Hung, Andrew J.
• Is Part Of: Current opinion in urology. Volume 31:Issue 4(2021)
• Publication Date: 2021-07
• Subjects: artificial intelligence -- machine learning model -- surgical skills -- surgical training; Urology -- Periodicals; Review Literature -- Bibliography; Review Literature -- Periodicals; Male Urogenital Diseases -- Bibliography; Male Urogenital Diseases -- Periodicals; Female Urogenital Diseases -- Bibliography; Female Urogenital Diseases -- Periodicals; Urologic Diseases -- Bibliography; Urologic Diseases -- Periodicals; Periodicals; 616.6005
• DOI: 10.1097/MOU.0000000000000890
• Languages: English
• ISSNs: 1473-6586

Abstract

Abstract : Purpose of review: As technology advances, surgical training has evolved in parallel over the previous decade. Training is commonly seen as a way to prepare surgeons for their day-to-day work; however, more importantly, it allows for certification of skills to ensure maximum patient safety. This article reviews advances in the use of machine learning and artificial intelligence for improvements of surgical skills in urology. Recent findings: Six studies have been published, which met the inclusion criteria. All articles assessed the application of artificial intelligence in improving surgical training. Different approaches were taken, such as using machine learning to identify and classify suturing gestures, creating automated objective evaluation reports, and determining surgical technical skill levels to predict clinical outcomes. The articles illustrated the continuously growing role of artificial intelligence to address the difficulties currently present in evaluating urological surgical skills. Summary: Artificial intelligence allows us to efficiently analyze the surmounting data related to surgical training and use it to come to conclusions that normally would require human intelligence. Although these metrics have been shown to predict surgeon expertise and surgical outcomes, evidence is still scarce regarding their ability to directly improve patient outcomes. Considering this, current active research is growing on the topic of deep learning-based computer vision to provide automated metrics needed for real-time surgeon feedback.