Assisted phase and step annotation for surgical videos

Lécuyer, Gurvan; Ragot, Martin; Martin, Nicolas; Launay, Laurent; Jannin, Pierre

doi:10.1007/s11548-019-02108-8

Cited by 36 publications

(25 citation statements)

References 20 publications

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“…The subjectivity of annotators can distort the data used for AI training, leading to systematic errors in surgical AI applications (21). Although most raw data require time-consuming, labor-driven annotation for being converted into AI-recognizable resources, AI could also help with the labeling process in saving time by improving manual annotation accuracy and speeding up labeling (66). Moreover, AI can also be used to standardize the documentation of surgical videos (59).…”

Section: Annotation Of Pretraining Data For Aimentioning

confidence: 99%

Artificial intelligence assisted display in thoracic surgery: development and possibilities

Chen¹,

Zhang²,

Yan³

et al. 2021

J Thorac Dis

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Section: Annotation Of Pretraining Data For Aimentioning

confidence: 99%

Artificial intelligence assisted display in thoracic surgery: development and possibilities

Chen¹,

Zhang²,

Yan³

et al. 2021

J Thorac Dis

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“…The agreed “core” steps can be used for education (e.g. operative video annotation), surgical skills assessment, and the development of models and simulators [ 13 , 19 , 22 , 38 ]. Similarly, the agreed “optional” steps highlight areas of heterogeneity of practice that will benefit from further research—most notably in skull base reconstruction (closure phase) and surgical exposure (nasal, sphenoid, sellar phases) [ 2 , 3 , 5 , 7 , 39 ].…”

Section: Discussionmentioning

confidence: 99%

Pituitary society expert Delphi consensus: operative workflow in endoscopic transsphenoidal pituitary adenoma resection

et al. 2021

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Purpose Surgical workflow analysis seeks to systematically break down operations into hierarchal components. It facilitates education, training, and understanding of surgical variations. There are known educational demands and variations in surgical practice in endoscopic transsphenoidal approaches to pituitary adenomas. Through an iterative consensus process, we generated a surgical workflow reflective of contemporary surgical practice. Methods A mixed-methods consensus process composed of a literature review and iterative Delphi surveys was carried out within the Pituitary Society. Each round of the survey was repeated until data saturation and > 90% consensus was reached. Results There was a 100% response rate and no attrition across both Delphi rounds. Eighteen international expert panel members participated. An extensive workflow of 4 phases (nasal, sphenoid, sellar and closure) and 40 steps, with associated technical errors and adverse events, were agreed upon by 100% of panel members across rounds. Both core and case-specific or surgeon-specific variations in operative steps were captured. Conclusions Through an international expert panel consensus, a workflow for the performance of endoscopic transsphenoidal pituitary adenoma resection has been generated. This workflow captures a wide range of contemporary operative practice. The agreed “core” steps will serve as a foundation for education, training, assessment and technological development (e.g. models and simulators). The “optional” steps highlight areas of heterogeneity of practice that will benefit from further research (e.g. methods of skull base repair). Further adjustments could be made to increase applicability around the world.

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“…First, they can facilitate more ubiquitous use of video review by augmenting or automating the time-consuming tasks associated with cutting, tagging, splicing, and binning surgical video. 38 Second, by effortlessly combing through thousands of hours of surgical video, carefully tuned algorithms have the potential to help clarify what techniques and decisions are relevant to the outcomes we care most about. In pursuit of these more advanced analytic capabilities, our team at Stanford University is collaborating with experts in artificial intelligence, computer vision, and mechanical engineering to learn how we can bring these capabilities to the operating room.…”

Section: "Most Of the Time We Have A Good Feel Of The Game So The Analytics Confirm What We Already Know" -Tierna Davidson Center Back Usmentioning

confidence: 99%

Mastering the thousand tiny details: routine use of video to optimize performance in sport and in surgery

Jopling

Visser

2021

ANZ Journal of Surgery

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Assisted phase and step annotation for surgical videos

Cited by 36 publications

References 20 publications

Artificial intelligence assisted display in thoracic surgery: development and possibilities

Artificial intelligence assisted display in thoracic surgery: development and possibilities

Pituitary society expert Delphi consensus: operative workflow in endoscopic transsphenoidal pituitary adenoma resection

Mastering the thousand tiny details: routine use of video to optimize performance in sport and in surgery

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