Towards automatic verification of the critical view of the myopectineal orifice with artificial intelligence

Takeuchi, Masashi; Collins, Toby; Lipps, Clement; Haller, Mathieu; Uwineza, Josiane; Okamoto, Nariaki; Nkusi, Richard; Marescaux, Jacques; Kawakubo, Hirofumi; Kitagawa, Yuko; González, Cristians; Mutter, Didier; Perretta, Silvana; Hostettler, Alexandre; Dallemagne, Bernard

doi:10.1007/s00464-023-09934-7

Cited by 7 publications

(4 citation statements)

References 38 publications

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“…AI enhances these systems by providing real‐time analysis and precision in movement and learning from each surgery performed, thereby improving the outcomes over time. In particular, advanced image recognition powered by AI algorithms aids in quickly identifying critical anatomical structures, navigating complicated anatomy more easily, and reducing the risk of developing surgical complications 3,4 . In addition, AI's ability to understand and analyze surgical process facilitates seamless information sharing across the surgical team, contributing to the early detection of complications and ensuring rapid intervention 5 .…”

Section: Robotic Surgery and Ai Navigationmentioning

confidence: 99%

Artificial intelligence and surgery

Takeuchi,

Kitagawa

2023

Annals of Gastroent Surgery

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Section: Robotic Surgery and Ai Navigationmentioning

confidence: 99%

Artificial intelligence and surgery

Takeuchi,

Kitagawa

2023

Annals of Gastroent Surgery

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“…AI is readily being used in imaging and video analysis with the capability of providing real‐time clinical decision making in multiple procedures such as endoscopic tumor diagnosis, laparoscopic cholecystectomy anatomic recognition, and pulmonary lesion detection 20–22 . We earlier demonstrated the usability for AI in surgical fields for both laparoscopic and robotic approaches 23–27 . Based on our prior research experience, we hypothesized that this state‐of‐the‐art technology can be applied to the evaluation of surgical skill.…”

Section: Introductionmentioning

confidence: 99%

“… 20 , 21 , 22 We earlier demonstrated the usability for AI in surgical fields for both laparoscopic and robotic approaches. 23 , 24 , 25 , 26 , 27 Based on our prior research experience, we hypothesized that this state‐of‐the‐art technology can be applied to the evaluation of surgical skill. So far, two different strategies have been implemented by institutions including our own aimed to assess surgical skill.…”

Section: Introductionmentioning

confidence: 99%

Evaluating surgical expertise with AI‐based automated instrument recognition for robotic distal gastrectomy

Strong,

Furube,

Takeuchi

et al. 2024

Annals of Gastroent Surgery

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IntroductionComplexities of robotic distal gastrectomy (RDG) give reason to assess physician's surgical skill. Varying levels in surgical skill affect patient outcomes. We aim to investigate how a novel artificial intelligence (AI) model can be used to evaluate surgical skill in RDG by recognizing surgical instruments.MethodsFifty‐five consecutive robotic surgical videos of RDG for gastric cancer were analyzed. We used Deeplab, a multi‐stage temporal convolutional network, and it trained on 1234 manually annotated images. The model was then tested on 149 annotated images for accuracy. Deep learning metrics such as Intersection over Union (IoU) and accuracy were assessed, and the comparison between experienced and non‐experienced surgeons based on usage of instruments during infrapyloric lymph node dissection was performed.ResultsWe annotated 540 Cadiere forceps, 898 Fenestrated bipolars, 359 Suction tubes, 307 Maryland bipolars, 688 Harmonic scalpels, 400 Staplers, and 59 Large clips. The average IoU and accuracy were 0.82 ± 0.12 and 87.2 ± 11.9% respectively. Moreover, the percentage of each instrument's usage to overall infrapyloric lymphadenectomy duration predicted by AI were compared. The use of Stapler and Large clip were significantly shorter in the experienced group compared to the non‐experienced group.ConclusionsThis study is the first to report that surgical skill can be successfully and accurately determined by an AI model for RDG. Our AI gives us a way to recognize and automatically generate instance segmentation of the surgical instruments present in this procedure. Use of this technology allows unbiased, more accessible RDG surgical skill.

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“…Still, the choice of surgical technique for groin hernia repair remains controversial due to the technical skills required to perform minimally invasive, as compared to conventional, techniques [ 19 , 23 ]. Previous studies on automated step detection in hernia surgery focused mainly on transabdominal preperitoneal repair (TAPP) [ 24 , 25 ]. The aim of this study was to develop, test, and demonstrate the accuracy of a novel computer vision algorithm for automated surgical step detection of laparoscopic totally extraperitoneal (TEP) inguinal hernia repair.…”

mentioning

confidence: 99%

A novel high accuracy model for automatic surgical workflow recognition using artificial intelligence in laparoscopic totally extraperitoneal inguinal hernia repair (TEP)

Ortenzi,

Rapoport Ferman,

Antolin

et al. 2023

Surg Endosc

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Introduction Artificial intelligence and computer vision are revolutionizing the way we perceive video analysis in minimally invasive surgery. This emerging technology has increasingly been leveraged successfully for video segmentation, documentation, education, and formative assessment. New, sophisticated platforms allow pre-determined segments chosen by surgeons to be automatically presented without the need to review entire videos. This study aimed to validate and demonstrate the accuracy of the first reported AI-based computer vision algorithm that automatically recognizes surgical steps in videos of totally extraperitoneal (TEP) inguinal hernia repair. Methods Videos of TEP procedures were manually labeled by a team of annotators trained to identify and label surgical workflow according to six major steps. For bilateral hernias, an additional change of focus step was also included. The videos were then used to train a computer vision AI algorithm. Performance accuracy was assessed in comparison to the manual annotations. Results A total of 619 full-length TEP videos were analyzed: 371 were used to train the model, 93 for internal validation, and the remaining 155 as a test set to evaluate algorithm accuracy. The overall accuracy for the complete procedure was 88.8%. Per-step accuracy reached the highest value for the hernia sac reduction step (94.3%) and the lowest for the preperitoneal dissection step (72.2%). Conclusions These results indicate that the novel AI model was able to provide fully automated video analysis with a high accuracy level. High-accuracy models leveraging AI to enable automation of surgical video analysis allow us to identify and monitor surgical performance, providing mathematical metrics that can be stored, evaluated, and compared. As such, the proposed model is capable of enabling data-driven insights to improve surgical quality and demonstrate best practices in TEP procedures. Graphical abstract

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Towards automatic verification of the critical view of the myopectineal orifice with artificial intelligence

Cited by 7 publications

References 38 publications

Artificial intelligence and surgery

Artificial intelligence and surgery

Evaluating surgical expertise with AI‐based automated instrument recognition for robotic distal gastrectomy

A novel high accuracy model for automatic surgical workflow recognition using artificial intelligence in laparoscopic totally extraperitoneal inguinal hernia repair (TEP)

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