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      Artificial Intelligence in Surgery and Its Potential for Gastric Cancer

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          Abstract

          Artificial intelligence (AI) has made significant progress in recent years, and many medical fields are attempting to introduce AI technology into clinical practice. Currently, much research is being conducted to evaluate that AI can be incorporated into surgical procedures to make them safer and more efficient, subsequently to obtain better outcomes for patients. In this paper, we review basic AI research regarding surgery and discuss the potential for implementing AI technology in gastric cancer surgery. At present, research and development is focused on AI technologies that assist the surgeon's understandings and judgment during surgery, such as anatomical navigation. AI systems are also being developed to recognize in which the surgical phase is ongoing. Such a surgical phase recognition systems is considered for effective storage of surgical videos and education, in the future, for use in systems to objectively evaluate the skill of surgeons. At this time, it is not considered practical to let AI make intraoperative decisions or move forceps automatically from an ethical standpoint, too. At present, AI research on surgery has various limitations, and it is desirable to develop practical systems that will truly benefit clinical practice in the future.

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          Most cited references39

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          Artificial Intelligence and Surgical Decision-Making

          Surgeons make complex, high-stakes decisions under time constraints and uncertainty, with significant effect on patient outcomes. This review describes the weaknesses of traditional clinical decision-support systems and proposes that artificial intelligence should be used to augment surgical decision-making.
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            Computer Vision Analysis of Intraoperative Video: Automated Recognition of Operative Steps in Laparoscopic Sleeve Gastrectomy

            To develop and assess AI algorithms to identify operative steps in laparoscopic sleeve gastrectomy (LSG). Computer vision, a form of artificial intelligence (AI), allows for quantitative analysis of video by computers for identification of objects and patterns, such as in autonomous driving. Intraoperative video from LSG from an academic institution were annotated by two fellowship-trained, board-certified bariatric surgeons. Videos were segmented into the following steps: 1) port placement, 2) liver retraction, 3) liver biopsy, 4) gastrocolic ligament dissection, 5) stapling of the stomach, 6) bagging specimen, and 7) final inspection of staple line. Deep neural networks were used to analyze videos. Accuracy of operative step identification by the AI was determined by comparing to surgeon annotations. 88 cases of LSG were analyzed. A random 70% sample of these clips were used to train the AI and 30% to test the AI’s performance. Mean concordance correlation coefficient for human annotators was 0.862, suggesting excellent agreement. Mean (±SD) accuracy of the AI in identifying operative steps in the test set was 82% ± 4% with a maximum of 85.6%. AI can extract quantitative surgical data from video with 85.6% accuracy. This suggests operative video could be used as a quantitative data source for research in intraoperative clinical decision support, risk prediction, or outcomes studies. The goal of this study was to develop and assess AI algorithms to identify operative steps in laparoscopic sleeve gastrectomy (LSG). 88 videos were analyzed. Mean accuracy of the algorithms was 82% ± 4% in identifying steps of LSG.
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              Surgical process modelling: a review.

              Surgery is continuously subject to technological and medical innovations that are transforming daily surgical routines. In order to gain a better understanding and description of surgeries, the field of surgical process modelling (SPM) has recently emerged. The challenge is to support surgery through the quantitative analysis and understanding of operating room activities. Related surgical process models can then be introduced into a new generation of computer-assisted surgery systems.
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                Author and article information

                Journal
                J Gastric Cancer
                J Gastric Cancer
                JGC
                Journal of Gastric Cancer
                The Korean Gastric Cancer Association
                2093-582X
                2093-5641
                July 2023
                26 July 2023
                : 23
                : 3
                : 400-409
                Affiliations
                Gastric Surgery Division, National Cancer Center Hospital East, Kashiwa, Japan.
                Author notes
                Correspondence to Takahiro Kinoshita. Gastric Surgery Division, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa 277-8577, Japan. takkinos@ 123456east.ncc.go.jp
                Author information
                https://orcid.org/0000-0001-7365-8733
                https://orcid.org/0000-0002-5505-0406
                Article
                10.5230/jgc.2023.23.e27
                10412972
                37553128
                016894d9-3ece-4038-9175-5c540c953a6d
                Copyright © 2023. Korean Gastric Cancer Association

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( https://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 15 July 2023
                : 19 July 2023
                : 20 July 2023
                Categories
                Review Article

                Oncology & Radiotherapy
                artificial intelligence,gastric cancer,surgery,minimally invasive surgery

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