Current applications of artificial intelligence-based computer vision in laparoscopic surgery
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-bas...
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| Published in | Laparoscopic, endoscopic, and robotic surgery Vol. 6; no. 3; pp. 91 - 96 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.09.2023
KeAi Communications Co., Ltd |
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| 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. |
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| AbstractList | 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. |
| Author | Qian, Yinling Guo, Kangwei Fang, Chihua Yang, Jian Tao, Haisu Zhu, Yilin Li, Baihong |
| Author_xml | – sequence: 1 givenname: Kangwei orcidid: 0000-0001-7199-9382 surname: Guo fullname: Guo, Kangwei organization: The First Department of Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, China – sequence: 2 givenname: Haisu surname: Tao fullname: Tao, Haisu organization: The First Department of Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, China – sequence: 3 givenname: Yilin orcidid: 0009-0000-7282-8489 surname: Zhu fullname: Zhu, Yilin organization: The Second Clinical Medical College of Southern Medical University, Guangzhou 510280, China – sequence: 4 givenname: Baihong surname: Li fullname: Li, Baihong organization: The First Department of Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, China – sequence: 5 givenname: Chihua surname: Fang fullname: Fang, Chihua organization: The First Department of Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, China – sequence: 6 givenname: Yinling surname: Qian fullname: Qian, Yinling email: yl.qian@siat.ac.cn organization: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China – sequence: 7 givenname: Jian surname: Yang fullname: Yang, Jian email: yangjian486@126.com organization: The First Department of Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, China |
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| Keywords | Deep learning Computer vision Laparoscopic surgery Artificial intelligence |
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