Deep learning for video-based assessment of endotracheal intubation skills
This system's ability to accurately differentiate between experts and novices instills confidence in its effectiveness and potential to improve training and certification processes for healthcare providers.
Authors
- Lora Cavuoto (ORCID: https://orcid.org/0000-0003-4717-8378)
- Jean-Paul Ainam (ORCID: https://orcid.org/0000-0001-8081-6867)
- Taylor Kunkes (ORCID: https://orcid.org/0000-0001-5795-5789)
- Jack Norfleet (ORCID: https://orcid.org/0000-0001-8643-0973)
- Matthew Hackett
- Brian M. Clemency (ORCID: https://orcid.org/0000-0002-7610-3087)
- Erim Yanık (ORCID: https://orcid.org/0000-0001-8290-2144)
- Rahul Kumar (ORCID: https://orcid.org/0000-0001-9293-6903)
- Suvranu De (ORCID: https://orcid.org/0000-0001-8489-0001)
- Kaori Tanaka (ORCID: https://orcid.org/0009-0009-0217-3849)
Institutions
- Florida State University (US)
- Florida A&M University - Florida State University College of Engineering (US)
- Rensselaer Polytechnic Institute (US)
- United States Army Combat Capabilities Development Command (US)
- United States Army Futures Command (US)
- University at Buffalo, State University of New York (US)
Publication Details
- Journal
- Communications Medicine
- Published
- 2025-04-14
- DOI
- https://doi.org/10.1038/s43856-025-00776-z
- Primary Topic
- Airway Management and Intubation Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- U.S. Army Combat Capabilities Development Command Soldier Center