Automated surgical workflow identification by artificial intelligence in laparoscopic hepatectomy: Experimental research

Sasaki, Kimimasa; Ito, Masaaki; Kobayashi, Shin; Kitaguchi, Daichi; Matsuzaki, Hiroyuki; Kudo, Masashi; Hasegawa, Hiro; Takeshita, Nobuyoshi; Sugimoto, Motokazu; Mitsunaga, Shuichi; Gotohda, Naoto

doi:10.1016/j.ijsu.2022.106856

Cited by 11 publications

(2 citation statements)

References 26 publications

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“…Laparoscopic cholecystectomy is the most commonly used procedure to test automated surgical workflow recognition models 4 . Other procedures that have been used for this task include liver surgery 27 , gynecologic surgery 28 , colorectal surgery 29 , bariatric surgery 30 , and urological surgery 31 . In our study, owing to the standardized process of RLLS, multilevel surgical workflow recognition achieved relatively high accuracy.…”

Section: Discussionmentioning

confidence: 99%

Multi-level effective surgical workflow recognition in robotic left lateral sectionectomy with deep learning: Experimental research

Liu

Zhao

Zhang

et al. 2023

International Journal of Surgery

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Background: Automated surgical workflow recognition is the foundation for computational models of medical knowledge to interpret surgical procedures. The fine-grained segmentation of the surgical process and the improvement of the accuracy of surgical workflow recognition facilitate the realization of autonomous robotic surgery. This study aimed to construct a multigranularity temporal annotation dataset of the standardized robotic left lateral sectionectomy (RLLS) and develop a deep learning-based automated model for multilevel overall and effective surgical workflow recognition. Methods: From December 2016 to May 2019, 45 cases of RLLS videos were enrolled in our dataset. All frames of RLLS videos in this study are labeled with temporal annotations. The authors defined those activities that truly contribute to the surgery as effective frames, while other activities are labeled as under-effective frames. Effective frames of all RLLS videos are annotated with three hierarchical levels of 4 steps, 12 tasks, and 26 activities. A hybrid deep learning model were used for surgical workflow recognition of steps, tasks, activities, and under-effective frames. Moreover, the authors also carried out multilevel effective surgical workflow recognition after removing under-effective frames. Results: The dataset comprises 4 383 516 annotated RLLS video frames with multilevel annotation, of which 2 418 468 frames are effective. The overall accuracies of automated recognition for Steps, Tasks, Activities, and under-effective frames are 0.82, 0.80, 0.79, and 0.85, respectively, with corresponding precision values of 0.81, 0.76, 0.60, and 0.85. In multilevel effective surgical workflow recognition, the overall accuracies were increased to 0.96, 0.88, and 0.82 for Steps, Tasks, and Activities, respectively, while the precision values were increased to 0.95, 0.80, and 0.68. Conclusion: In this study, the authors created a dataset of 45 RLLS cases with multilevel annotations and developed a hybrid deep learning model for surgical workflow recognition. The authors demonstrated a fairly higher accuracy in multilevel effective surgical workflow recognition when under-effective frames were removed. Our research could be helpful in the development of autonomous robotic surgery.

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Section: Discussionmentioning

confidence: 99%

Multi-level effective surgical workflow recognition in robotic left lateral sectionectomy with deep learning: Experimental research

Liu

Zhao

Zhang

et al. 2023

International Journal of Surgery

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“…They also reported a model for transanal endoscopic total mesorectal excision with an accuracy rate of 93.2% [ 24 ]. Sasaki et al [ 25 ] reported a phase recognition model for laparoscopic liver resection with an accuracy rate of 94.7%. Takeuchi et al [ 26 ] reported about robotic esophagectomy with an accuracy rate of 84%.…”

Section: Surgery Phase Recognitionmentioning

confidence: 99%

Artificial Intelligence in Surgery and Its Potential for Gastric Cancer

Kinoshita

Komatsu

2023

J Gastric Cancer

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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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Intraoperative artificial intelligence system identifying liver vessels in laparoscopic liver resection: a retrospective experimental study

Une,

Kobayashi,

Kitaguchi

et al. 2024

Surg Endosc

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Automated surgical workflow identification by artificial intelligence in laparoscopic hepatectomy: Experimental research

Cited by 11 publications

References 26 publications

Multi-level effective surgical workflow recognition in robotic left lateral sectionectomy with deep learning: Experimental research

Multi-level effective surgical workflow recognition in robotic left lateral sectionectomy with deep learning: Experimental research

Artificial Intelligence in Surgery and Its Potential for Gastric Cancer

Intraoperative artificial intelligence system identifying liver vessels in laparoscopic liver resection: a retrospective experimental study

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