Kangwei Guo , Haisu Tao , Yilin Zhu , Baihong Li , Chihua Fang , Yinling Qian , Jian Yang
{"title":"Current applications of artificial intelligence-based computer vision in laparoscopic surgery","authors":"Kangwei Guo , Haisu Tao , Yilin Zhu , Baihong Li , Chihua Fang , Yinling Qian , Jian Yang","doi":"10.1016/j.lers.2023.07.001","DOIUrl":null,"url":null,"abstract":"<div><p>Recent advances in artificial intelligence (AI) have sparked a surge in the application of computer vision (CV) in surgical video analysis. Laparoscopic surgery produces a large number of surgical videos, which provides a new opportunity for improving of CV technology in laparoscopic surgery. AI-based CV techniques may leverage these surgical video data to develop real-time automated decision support tools and surgeon training systems, which shows a new direction in dealing with the shortcomings of laparoscopic surgery. The effectiveness of CV applications in surgical procedures is still under early evaluation, so it is necessary to discuss challenges and obstacles. The review introduced the commonly used deep learning algorithms in CV and described their usage in detail in four application scenes, including phase recognition, anatomy detection, instrument detection and action recognition in laparoscopic surgery. The currently described applications of CV in laparoscopic surgery are limited. Most of the current research focuses on the identification of workflow and anatomical structure, while the identification of instruments and surgical actions is still awaiting further breakthroughs. Future research on the use of CV in laparoscopic surgery should focus on applications in more scenarios, such as surgeon skill assessment and the development of more efficient models.</p></div>","PeriodicalId":32893,"journal":{"name":"Laparoscopic Endoscopic and Robotic Surgery","volume":"6 3","pages":"Pages 91-96"},"PeriodicalIF":0.0000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Laparoscopic Endoscopic and Robotic Surgery","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468900923000403","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 2
Abstract
Recent advances in artificial intelligence (AI) have sparked a surge in the application of computer vision (CV) in surgical video analysis. Laparoscopic surgery produces a large number of surgical videos, which provides a new opportunity for improving of CV technology in laparoscopic surgery. AI-based CV techniques may leverage these surgical video data to develop real-time automated decision support tools and surgeon training systems, which shows a new direction in dealing with the shortcomings of laparoscopic surgery. The effectiveness of CV applications in surgical procedures is still under early evaluation, so it is necessary to discuss challenges and obstacles. The review introduced the commonly used deep learning algorithms in CV and described their usage in detail in four application scenes, including phase recognition, anatomy detection, instrument detection and action recognition in laparoscopic surgery. The currently described applications of CV in laparoscopic surgery are limited. Most of the current research focuses on the identification of workflow and anatomical structure, while the identification of instruments and surgical actions is still awaiting further breakthroughs. Future research on the use of CV in laparoscopic surgery should focus on applications in more scenarios, such as surgeon skill assessment and the development of more efficient models.
期刊介绍:
Laparoscopic, Endoscopic and Robotic Surgery aims to provide an academic exchange platform for minimally invasive surgery at an international level. We seek out and publish the excellent original articles, reviews and editorials as well as exciting new techniques to promote the academic development.
Topics of interests include, but are not limited to:
▪ Minimally invasive clinical research mainly in General Surgery, Thoracic Surgery, Urology, Neurosurgery, Gynecology & Obstetrics, Gastroenterology, Orthopedics, Colorectal Surgery, Otolaryngology, etc.;
▪ Basic research in minimally invasive surgery;
▪ Research of techniques and equipments in minimally invasive surgery, and application of laparoscopy, endoscopy, robot and medical imaging;
▪ Development of medical education in minimally invasive surgery.