A Novel Dissection Gesture Classification to Characterize Robotic Dissection Technique for Renal Hilar Dissection

Ma, Runzhuo; Vanstrum, Erik; Nguyen, Jessica; Chen, Andrew; Chen, Jian; Hung, Andrew J.

doi:10.1097/ju.0000000000001328

Cited by 11 publications

(19 citation statements)

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“…Although these dissection steps are procedure-specific, they can be performed with a common vocabulary of discrete dissection actions, or gestures. In fact, there is an established dissection gesture taxonomy 29 which can be used to annotate such steps. We include such annotation details in the Methods section.…”

Section: Dissection Stepmentioning

confidence: 99%

“…RAPN -USC We collected 27 surgical videos of the RAPN procedure from 16 surgeons at USC. Each video segment (n = 339) was annotated with one of 5 dissection gestures (based on the established gesture taxonomy 29 ). For this renal hilar dissection step, Hung et al 29 previously demonstrated that the efficiency with which these gestures are performed can delineate between experienced and novice surgeons.…”

Section: /15mentioning

confidence: 99%

“…Each video segment (n = 339) was annotated with one of 5 dissection gestures (based on the established gesture taxonomy 29 ). For this renal hilar dissection step, Hung et al 29 previously demonstrated that the efficiency with which these gestures are performed can delineate between experienced and novice surgeons. In this setting, our machine learning task of interest remains multi-class gesture classification.…”

Section: /15mentioning

confidence: 99%

“…Gesture Annotations A group of medical students was provided with an instructional manual which delineated the different categories of suturing and dissection gestures. This manual was based on a previously-outlined and peer-reviewed gesture taxonomy 29 . Once the training phase of the annotation process was complete, we provided the medical students with entire surgical videos (e.g., entire NS step) to be annotated with the category of the gesture in addition to the startand end-time of the surgical video segment.…”

Section: Ground-truth Annotationsmentioning

confidence: 99%

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Quantification of Robotic Surgeries with Vision-Based Deep Learning

Kiyasseh¹,

Ma²,

Haque³

et al. 2022

Preprint

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Surgery is a high-stakes domain where surgeons must navigate critical anatomical structures and actively avoid potential complications while achieving the main task at hand. Such surgical activity has been shown to affect long-term patient outcomes. To better understand this relationship, whose mechanics remain unknown for the majority of surgical procedures, we hypothesize that the core elements of surgery must first be quantified in a reliable, objective, and scalable manner. We believe this is a prerequisite for the provision of surgical feedback and modulation of surgeon performance in pursuit of improved patient outcomes. To holistically quantify surgeries, we propose a unified deep learning framework, entitled Roboformer, which operates exclusively on videos recorded during surgery to independently achieve multiple tasks: surgical phase recognition (the what of surgery), gesture classification and skills assessment (the how of surgery). We validated our framework on four video-based datasets of two commonly-encountered types of steps (dissection and suturing) within minimally-invasive robotic surgeries. We demonstrated that our framework can generalize well to unseen videos, surgeons, medical centres, and surgical procedures. We also found that our framework, which naturally lends itself to explainable findings, identified relevant information when achieving a particular task. These findings are likely to instill surgeons with more confidence in our framework's behaviour, increasing the likelihood of clinical adoption, and thus paving the way for more targeted surgical feedback.

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Section: Dissection Stepmentioning

confidence: 99%

Section: /15mentioning

confidence: 99%

Section: /15mentioning

confidence: 99%

Section: Ground-truth Annotationsmentioning

confidence: 99%

See 2 more Smart Citations

Quantification of Robotic Surgeries with Vision-Based Deep Learning

Kiyasseh¹,

Ma²,

Haque³

et al. 2022

Preprint

Self Cite

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“…Surgical gestures, defined as the smallest meaningful interaction of a surgical instrument with human tissue 4,5 , are a novel approach to deconstruct surgery. They have the potential to objectively quantify surgery meanwhile provide actionable feedback for trainees.…”

Section: Introductionmentioning

confidence: 99%

Surgical gestures as a method to quantify surgical performance and predict patient outcomes

Ramaswamy

et al. 2022

npj Digit. Med.

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How well a surgery is performed impacts a patient’s outcomes; however, objective quantification of performance remains an unsolved challenge. Deconstructing a procedure into discrete instrument-tissue “gestures” is a emerging way to understand surgery. To establish this paradigm in a procedure where performance is the most important factor for patient outcomes, we identify 34,323 individual gestures performed in 80 nerve-sparing robot-assisted radical prostatectomies from two international medical centers. Gestures are classified into nine distinct dissection gestures (e.g., hot cut) and four supporting gestures (e.g., retraction). Our primary outcome is to identify factors impacting a patient’s 1-year erectile function (EF) recovery after radical prostatectomy. We find that less use of hot cut and more use of peel/push are statistically associated with better chance of 1-year EF recovery. Our results also show interactions between surgeon experience and gesture types—similar gesture selection resulted in different EF recovery rates dependent on surgeon experience. To further validate this framework, two teams independently constructe distinct machine learning models using gesture sequences vs. traditional clinical features to predict 1-year EF. In both models, gesture sequences are able to better predict 1-year EF (Team 1: AUC 0.77, 95% CI 0.73–0.81; Team 2: AUC 0.68, 95% CI 0.66–0.70) than traditional clinical features (Team 1: AUC 0.69, 95% CI 0.65–0.73; Team 2: AUC 0.65, 95% CI 0.62–0.68). Our results suggest that gestures provide a granular method to objectively indicate surgical performance and outcomes. Application of this methodology to other surgeries may lead to discoveries on methods to improve surgery.

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Assessing the efficacy of dissection gestures in robotic surgery

Inouye

Nguyen

et al. 2022

J Robotic Surg

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A Novel Dissection Gesture Classification to Characterize Robotic Dissection Technique for Renal Hilar Dissection

Cited by 11 publications

References 0 publications

Quantification of Robotic Surgeries with Vision-Based Deep Learning

Quantification of Robotic Surgeries with Vision-Based Deep Learning

Surgical gestures as a method to quantify surgical performance and predict patient outcomes

Assessing the efficacy of dissection gestures in robotic surgery

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