A systematic review on artificial intelligence in robot-assisted surgery. (November 2021)

• Record Type: Journal Article
• Authors: Moglia, Andrea; Georgiou, Konstantinos; Georgiou, Evangelos; Satava, Richard M.; Cuschieri, Alfred
• Is Part Of: International journal of surgery. Volume 95(2021)
• Publication Date: 2021-11
• Subjects: Artificial intelligence robot-assisted surgery -- Artificial intelligence robotic surgery -- Machine learning robot-assisted surgery -- Deep learning robot-assisted surgery -- Computer vision robotic surgery -- Urology robotic surgery -- General surgery robot surgery; Surgery -- Periodicals; Surgical Procedures, Operative -- Periodicals; 617.005
• DOI: 10.1016/j.ijsu.2021.106151
• Languages: English
• ISSNs: 1743-9191

Abstract

Abstract: Background: Despite the extensive published literature on the significant potential of artificial intelligence (AI) there are no reports on its efficacy in improving patient safety in robot-assisted surgery (RAS). The purposes of this work are to systematically review the published literature on AI in RAS, and to identify and discuss current limitations and challenges. Materials and methods: A literature search was conducted on PubMed, Web of Science, Scopus, and IEEExplore according to PRISMA 2020 statement. Eligible articles were peer-review studies published in English language from January 1, 2016 to December 31, 2020. Amstar 2 was used for quality assessment. Risk of bias was evaluated with the Newcastle Ottawa Quality assessment tool. Data of the studies were visually presented in tables using SPIDER tool. Results: Thirty-five publications, representing 3436 patients, met the search criteria and were included in the analysis. The selected reports concern: motion analysis (n = 17), urology (n = 12), gynecology (n = 1), other specialties (n = 1), training (n = 3), and tissue retraction (n = 1). Precision for surgical tools detection varied from 76.0% to 90.6%. Mean absolute error on prediction of urinary continence after robot-assisted radical prostatectomy (RARP) ranged from 85.9 to 134.7 days. Accuracy on prediction of length of stay after RARP was 88.5%. Accuracy on recognition of the next surgical task during robot-assisted partial nephrectomy (RAPN) achieved 75.7%. Conclusion: The reviewed studies were of low quality. The findings are limited by the small size of the datasets. Comparison between studies on the same topic was restricted due to algorithms and datasets heterogeneity. There is no proof that currently AI can identify the critical tasks of RAS operations, which determine patient outcome. There is an urgent need for studies on large datasets and external validation of the AI algorithms used. Furthermore, the results should be transparent and meaningful to surgeons, enabling them to inform patients in layman's words. Registration: Review Registry Unique Identifying Number: reviewregistry1225. Highlights: Several limitations and technical issues were found in the reviewed published studies. There is no proof that currently AI can recognize critical tasks of RAS operations. Studies on large datasets together with external validation of AI algorithms are needed. More transparent results are needed, which are meaningful to surgeons in future publications on AI. A glossary and an appendix for better understanding of the AI terms have been added.