Video labelling robot-assisted radical prostatectomy and the role of artificial intelligence (AI): training a novice

Abstract

Video labelling is the assigning of meaningful information to raw videos. With the evolution of artificial intelligence and its intended incorporation into the operating room, video datasets can be invaluable tools for education and the training of intelligent surgical workflow systems through computer vision. However, the process of manual labelling of video datasets can prove costly and time-consuming for already busy practising surgeons. Twenty-five robot-assisted radical prostatectomy (RARP) procedures were recorded on Proximie, an augmented reality platform, anonymised and access given to a novice, who was trained to develop the knowledge and skills needed to accurately segment a full-length RARP procedure on a video labelling platform. A labelled video was subsequently randomly selected for assessment of accuracy by four practising urologists. Of the 25 videos allocated, 17 were deemed suitable for labelling, and 8 were excluded on the basis of procedure length and video quality. The labelled video selected for assessment was graded for accuracy of temporal labelling, with an average score of 93.1%, and a range of 85.6–100%. The self-training of a novice in the accurate segmentation of a surgical video to the standard of a practising urologist is feasible and practical for the RARP procedure. The assigning of temporal labels on a video labelling platform was also studied and proved feasible throughout the study period.

A Systematic Review of New Imaging Technologies for Robotic Prostatectomy: From Molecular Imaging to Augmented Reality

“…The future in this process might be artificial-intelligence-based video-labeling. A preliminary study of Youssef et al demonstrates the feasibility of self-training of novices to surgical procedures to perform segmentation of RARP videos [ 52 ].…”

Automated Identification of Key Steps in Robotic-Assisted Radical Prostatectomy Using Artificial Intelligence

“…[14][15][16] Structured video with step labeling can help expedite video review among surgeons and trainees, but manually labeling surgical video footage with key steps is laborious, time intensive and expensive, which may limit its routine adoption. 5 The current study overcomes this obstacle by fully automating the process of video labeling using a novel AI platform, which may facilitate targeted video review of individual surgical steps (Figure 3, B).…”

“…However, the process of manually reviewing and labeling surgical videos is laborious and resource-intensive, thus limiting its routine and widespread use. 5 Currently, millions of surgeries are being performed and surgical video recorded globally each year, but the majority of that video is not being routinely or systematically analyzed, thus representing a tremendous untapped opportunity. We aim to develop an artificial intelligence (AI)‒enabled computer vision algorithm for automated identification of key surgical steps during RARP to serve as a framework for systematic and automated analysis of surgical video footage.…”

Artificial Intelligence in Urologic Robotic Oncologic Surgery: A Narrative Review

“…A study conducted by Youssef et al introduced an algorithm that integrated videolabelling into 25 robot-assisted radical prostatectomy (RARP) surgeries [15]. The videos were integrated into Proximie, an augmented reality platform, and provided to a novice surgeon for the purpose of annotating the 12 steps of the procedure [15]. Of the 25 films analysed, 17 were deemed appropriate for categorisation, while 8 were excluded due to their subpar quality and time-consuming nature [15].…”