Video-Based Surgical Coaching

Greenberg, Caprice C.; Dombrowski, Janet C.; Dimick, Justin B.

doi:10.1001/jamasurg.2015.4442

Cited by 111 publications

(38 citation statements)

References 6 publications

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“…Advances in machine learning and computer vision (CV) facilitate the discovery of unknown patterns in large bodies of visual data. [1][2][3][4][5] Applying these methods to analyze surgical video from the operating room may improve patient care by detecting complications, assisting trainee development, and assessing performance. [6][7][8][9][10] Algorithmic methods thrive when presented with comprehensive data sets.…”

Section: Introductionmentioning

confidence: 99%

Utility of the Simulated Outcomes Following Carotid Artery Laceration Video Data Set for Machine Learning Applications

et al. 2022

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Key Points Question What is the utility of a data set that contains videos of surgeons managing hemorrhage? Findings This quality improvement study of the Simulated Outcomes Following Carotid Artery Laceration (SOCAL), a public data set of surgeons managing catastrophic surgical hemorrhage in a cadaveric training exercise included 65 071 instrument annotations with recorded outcomes. Computer vision–based instrument detection achieved a mean average precision of 0.67 on SOCAL and a sensitivity of 0.77 and a positive predictive value of 0.96 at detecting surgical instruments from real intraoperative video. Meaning A corpus of videos of surgeons managing catastrophic hemorrhage is a novel, valuable resource for surgical data science.

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Section: Introductionmentioning

confidence: 99%

Utility of the Simulated Outcomes Following Carotid Artery Laceration Video Data Set for Machine Learning Applications

et al. 2022

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“…a HMM is only applicable to simple interrupted suturing tasks. Dimick, 2016;Hu et al, 2017;Soucisse et al, 2017). This study applied machine learning algorithms to classify surgical maneuvers and describe a participant's rate of progress through common benchtop suturing tasks.…”

Section: Discussionmentioning

confidence: 99%

Using Surgeon Hand Motions to Predict Surgical Maneuvers

Azari

Miller

et al. 2019

Hum Factors

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Objective: This study explores how common machine learning techniques can predict surgical maneuvers from a continuous video record of surgical benchtop simulations. Background: Automatic computer vision recognition of surgical maneuvers (suturing, tying, and transition) could expedite video review and objective assessment of surgeries. Method: We recorded hand movements of 37 clinicians performing simple and running subcuticular suturing benchtop simulations, and applied three machine learning techniques (decision trees, random forests, and hidden Markov models) to classify surgical maneuvers every 2 s (60 frames) of video. Results: Random forest predictions of surgical video correctly classified 74% of all video segments into suturing, tying, and transition states for a randomly selected test set. Hidden Markov model adjustments improved the random forest predictions to 79% for simple interrupted suturing on a subset of randomly selected participants. Conclusion: Random forest predictions aided by hidden Markov modeling provided the best prediction of surgical maneuvers. Training of models across all users improved prediction accuracy by 10% compared with a random selection of participants. Application: Marker-less video hand tracking can predict surgical maneuvers from a continuous video record with similar accuracy as robot-assisted surgical platforms, and may enable more efficient video review of surgical procedures for training and coaching.

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“…One study investigator (AD) recorded > 28 consecutive de-identified screening or surveillance colonoscopies per colonoscopist (October 3, 2016-November 11, 2016) and randomly selected seven colonoscopies per colonoscopist for CIQ evaluation using a random number generator. CIQ was graded using a scale developed by Rex 10 and adapted by Lee et al 11 by six U.S. gastroenterologists (RY, MB, AG, CK, TK, RK) with previous experience in colonoscopy quality. The methods for video recording, video selection, and CIQ evaluation have been previously described 11 .…”

Section: Methodsmentioning

confidence: 99%

“…Videotaping procedures is currently the most feasible way to obtain comprehensive data on technical skill. In the surgical field, assessing technical skill using video recordings has been utilized to identify variations in surgeon quality 9 and to provide individualized feedback to drive quality improvement efforts 10 . In gastrointestinal endoscopy, we and others have shown that assessments of technical skill via a structured review of videotaped colonoscopy procedures highly correlates with existing metrics of colonoscopy quality, including ADR and serrated polyp detection rates (SDR) 11 12 13 .…”

Section: Introductionmentioning

confidence: 99%

Individualized feedback on colonoscopy skills improves group colonoscopy quality in providers with lower adenoma detection rates

et al. 2022

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Background and study aims Colonoscopy inspection quality (CIQ) assesses skills (fold examination, cleaning, and luminal distension) during inspection for polyps and correlates with adenoma detection rate (ADR) and serrated detection rate (SDR). We aimed to determine whether providing individualized CIQ feedback with instructional videos improves quality metrics performance. Methods We prospectively studied 16 colonoscopists who already received semiannual benchmarked reports of quality metrics (ADR, SDR, and withdrawal time [WT]). We randomly selected seven colonoscopies/colonoscopist for evaluation. Six gastroenterologists graded CIQ using an established scale. We created instructional videos demonstrating optimal and poor inspection techniques. Colonoscopists received the instructional videos and benchmarked CIQ performance. We compared ADR, SDR, and WT in the 12 months preceding (“baseline”) and following CIQ feedback. Colonoscopists were stratified by baseline ADR into lower (≤ 34 %) and higher-performing (> 34 %) groups. Results Baseline ADR was 38.5 % (range 26.8 %–53.8 %) and SDR was 11.2 % (2.8 %–24.3 %). The proportion of colonoscopies performed by lower-performing colonoscopists was unchanged from baseline to post-CIQ feedback. All colonoscopists reviewed their CIQ report cards. Post-feedback, ADR (40.1 % vs 38.5 %, P = 0.1) and SDR (12.2 % vs. 11.2 %, P = 0.1) did not significantly improve; WT significantly increased (11.4 vs 12.4 min, P < 0.01). Among the eight lower-performing colonoscopists, group ADR (31.1 % vs 34.3 %, P = 0.02) and SDR (7.2 % vs 9.1 %, P = 0.02) significantly increased post-feedback. In higher-performing colonoscopists, ADR and SDR did not change. Conclusions CIQ feedback modestly improves ADR and SDR among colonoscopists with lower baseline ADR but has no effect on higher-performing colonoscopists. Individualized feedback on colonoscopy skills could be used to improve polyp detection by lower-performing colonoscopists.

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Video-Based Surgical Coaching

Cited by 111 publications

References 6 publications

Utility of the Simulated Outcomes Following Carotid Artery Laceration Video Data Set for Machine Learning Applications

Utility of the Simulated Outcomes Following Carotid Artery Laceration Video Data Set for Machine Learning Applications

Using Surgeon Hand Motions to Predict Surgical Maneuvers

Individualized feedback on colonoscopy skills improves group colonoscopy quality in providers with lower adenoma detection rates

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