Evolution of the digital operating room: the place of video technology in surgery.

PURPOSE: The aim of this review was to collate current evidence wherein digitalisation, through the incorporation of video technology and artificial intelligence (AI), is being applied to the practice of surgery. Applications are vast, and the literature investigating the utility of surgical video and its synergy with AI has steadily increased over the last 2 decades. This type of technology is widespread in other industries, such as autonomy in transportation and manufacturing. METHODS: Articles were identified primarily using the PubMed and MEDLINE databases. The MeSH terms used were "surgical education", "surgical video", "video labelling", "surgery", "surgical workflow", "telementoring", "telemedicine", "machine learning", "deep learning" and "operating room". Given the breadth of the subject and the scarcity of high-level data in certain areas, a narrative synthesis was selected over a meta-analysis or systematic review to allow for a focussed discussion of the topic. RESULTS: Three main themes were identified and analysed throughout this review, (1) the multifaceted utility of surgical video recording, (2) teleconferencing/telemedicine and (3) artificial intelligence in the operating room. CONCLUSIONS: Evidence suggests the routine collection of intraoperative data will be beneficial in the advancement of surgery, by driving standardised, evidence-based surgical care and personalised training of future surgeons. However, many barriers stand in the way of widespread implementation, necessitating close collaboration between surgeons, data scientists, medicolegal personnel and hospital policy makers.

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

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.