Identifying novel indicators of non-technical skills derived from operative video annotation
This study provides evidence that non-technical skills can be inferred from silent video of the surgical field alone. These findings lay the foundations for scalable, automated tools to evaluate surgeons' cognitive processes offering new avenues to improve surgical training, performance and outcomes.
Authors
- Daniel L. Beral
- Mohamed Abdulmajed
- Connor Boyle (ORCID: https://orcid.org/0000-0003-2124-607X)
- Victoria Ruth Tallentire (ORCID: https://orcid.org/0000-0002-9178-1425)
- Jeremy French (ORCID: https://orcid.org/0000-0002-3383-6985)
- Emma Howie (ORCID: https://orcid.org/0000-0003-1256-4295)
- Lachlan Dick
- Tim Stansfield (ORCID: https://orcid.org/0000-0002-5875-6279)
- Richard J. E. Skipworth (ORCID: https://orcid.org/0000-0003-1428-4078)
- Ahmed Saad (ORCID: https://orcid.org/0000-0002-5796-069X)
- J Wayman
- Paul Brennan (ORCID: https://orcid.org/0000-0001-8792-0499)
- Stuart Clark (ORCID: https://orcid.org/0000-0002-0495-670X)
- Douglas S. Smink
- Kamal Aryal
- Lasitha B Samarakoon
- Sara Downey
- James Tomlinson
- Leo McCormick Matthews
- the Surgical Video Grading and Assessment (SVGA) Group
- Steven Yule
- Joe Norton
- Ajay P Belgaumkar
- M J Proctor
- John V Taylor
- Euan Bright
- J A Young
- Leo R Brown
Institutions
- Brigham and Women's Hospital (US)
- NHS Lothian (GB)
- University of Edinburgh (GB)
Publication Details
- Journal
- British journal of surgery
- Published
- 2026-02-18
- DOI
- https://doi.org/10.1093/bjs/znag015
- Primary Topic
- Surgical Simulation and Training
- Type
- article
- Field-Weighted Citation Impact
- 0.00