Deep Learning Analysis of Surgical Video Recordings to Assess Nontechnical Skills

dc.citation.articleNumbere2422520en_US
dc.citation.issueNumber7en_US
dc.citation.journalTitleJAMA Network Openen_US
dc.citation.volumeNumber7en_US
dc.contributor.authorHarari, Rayan Ebnalien_US
dc.contributor.authorDias, Roger D.en_US
dc.contributor.authorKennedy-Metz, Lauren R.en_US
dc.contributor.authorVarni, Giovannaen_US
dc.contributor.authorGombolay, Matthewen_US
dc.contributor.authorYule, Stevenen_US
dc.contributor.authorSalas, Eduardoen_US
dc.contributor.authorZenati, Marco A.en_US
dc.date.accessioned2024-08-29T21:11:48Zen_US
dc.date.available2024-08-29T21:11:48Zen_US
dc.date.issued2024en_US
dc.description.abstractAssessing nontechnical skills in operating rooms (ORs) is crucial for enhancing surgical performance and patient safety. However, automated and real-time evaluation of these skills remains challenging.To explore the feasibility of using motion features extracted from surgical video recordings to automatically assess nontechnical skills during cardiac surgical procedures.This cross-sectional study used video recordings of cardiac surgical procedures at a tertiary academic US hospital collected from January 2021 through May 2022. The OpenPose library was used to analyze videos to extract body pose estimations of team members and compute various team motion features. The Non-Technical Skills for Surgeons (NOTSS) assessment tool was employed for rating the OR team’s nontechnical skills by 3 expert raters.NOTSS overall score, with motion features extracted from surgical videos as measures.A total of 30 complete cardiac surgery procedures were included: 26 (86.6%) were on-pump coronary artery bypass graft procedures and 4 (13.4%) were aortic valve replacement or repair procedures. All patients were male, and the mean (SD) age was 72 (6.3) years. All surgical teams were composed of 4 key roles (attending surgeon, attending anesthesiologist, primary perfusionist, and scrub nurse) with additional supporting roles. NOTSS scores correlated significantly with trajectory (r = 0.51, P = .005), acceleration (r = 0.48, P = .008), and entropy (r = −0.52, P = .004) of team displacement. Multiple linear regression, adjusted for patient factors, showed average team trajectory (adjusted R2 = 0.335; coefficient, 10.51 [95% CI, 8.81-12.21]; P = .004) and team displacement entropy (adjusted R2 = 0.304; coefficient, −12.64 [95% CI, −20.54 to −4.74]; P = .003) were associated with NOTSS scores.This study suggests a significant link between OR team movements and nontechnical skills ratings by NOTSS during cardiac surgical procedures, suggesting automated surgical video analysis could enhance nontechnical skills assessment. Further investigation across different hospitals and specialties is necessary to validate these findings.en_US
dc.identifier.citationHarari, R. E., Dias, R. D., Kennedy-Metz, L. R., Varni, G., Gombolay, M., Yule, S., Salas, E., & Zenati, M. A. (2024). Deep Learning Analysis of Surgical Video Recordings to Assess Nontechnical Skills. JAMA Network Open, 7(7), e2422520. https://doi.org/10.1001/jamanetworkopen.2024.22520en_US
dc.identifier.digitalharari_2024_oi_240721_1721767296-18704en_US
dc.identifier.doihttps://doi.org/10.1001/jamanetworkopen.2024.22520en_US
dc.identifier.urihttps://hdl.handle.net/1911/117735en_US
dc.language.isoengen_US
dc.publisherAmerican Medical Associationen_US
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution (CC BY) license.  Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleDeep Learning Analysis of Surgical Video Recordings to Assess Nontechnical Skillsen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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