Deep learning for video-based assessment of endotracheal intubation skills
Abstract Background Endotracheal intubation (ETI) is an emergency procedure performed in civilians and combat casualty care settings to establish an airway. It’s crucial that healthcare personnel are proficient in these skills, which traditionally have been evaluated through direct feedback from exp...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-025-00776-z |
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| author | Jean-Paul Ainam Erim Yanik Rahul Rahul Taylor Kunkes Lora Cavuoto Brian Clemency Kaori Tanaka Matthew Hackett Jack Norfleet Suvranu De |
| author_facet | Jean-Paul Ainam Erim Yanik Rahul Rahul Taylor Kunkes Lora Cavuoto Brian Clemency Kaori Tanaka Matthew Hackett Jack Norfleet Suvranu De |
| author_sort | Jean-Paul Ainam |
| collection | DOAJ |
| description | Abstract Background Endotracheal intubation (ETI) is an emergency procedure performed in civilians and combat casualty care settings to establish an airway. It’s crucial that healthcare personnel are proficient in these skills, which traditionally have been evaluated through direct feedback from experts. Unfortunately, this method can be inconsistent and subjective, requiring considerable time and resources. Methods This study introduces a system for assessing ETI skills using video analysis. The system employs advanced video processing techniques, including a 2D convolutional autoencoder (AE) based on a self-supervision model, capable of recognizing complex patterns in videos. A 1D convolutional model enhanced with a cross-view attention module then uses AE features to make assessments. Data for the study was gathered in two phases, focusing first on comparisons between experts and novices, and then examining how novices perform under time constraints with outcomes labeled as either successful or unsuccessful. A separate set of data using videos from head-mounted cameras was also analyzed. Results The system successfully distinguishes between experts and novices in initial trials and demonstrates high accuracy in further classifications, including under time pressure and using head-mounted camera footage. Conclusions 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. |
| format | Article |
| id | doaj-art-1ab36e4e674d4195add0fd06871ff585 |
| institution | DOAJ |
| issn | 2730-664X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Medicine |
| spelling | doaj-art-1ab36e4e674d4195add0fd06871ff5852025-08-20T03:18:23ZengNature PortfolioCommunications Medicine2730-664X2025-04-015111010.1038/s43856-025-00776-zDeep learning for video-based assessment of endotracheal intubation skillsJean-Paul Ainam0Erim Yanik1Rahul Rahul2Taylor Kunkes3Lora Cavuoto4Brian Clemency5Kaori Tanaka6Matthew Hackett7Jack Norfleet8Suvranu De9Center for Modeling, Simulation, & Imaging in Medicine, Rensselaer Polytechnic InstituteFlorida Agriculture & Mechanical University—Florida State University College of EngineeringCenter for Modeling, Simulation, & Imaging in Medicine, Rensselaer Polytechnic InstituteDepartment of Industrial and Systems Engineering, University at BuffaloDepartment of Industrial and Systems Engineering, University at BuffaloDepartment of Emergency Medicine, University at BuffaloDepartment of Emergency Medicine, University at BuffaloU.S. Army Futures Command, Combat Capabilities Development Command Soldier Center STTCU.S. Army Futures Command, Combat Capabilities Development Command Soldier Center STTCFlorida Agriculture & Mechanical University—Florida State University College of EngineeringAbstract Background Endotracheal intubation (ETI) is an emergency procedure performed in civilians and combat casualty care settings to establish an airway. It’s crucial that healthcare personnel are proficient in these skills, which traditionally have been evaluated through direct feedback from experts. Unfortunately, this method can be inconsistent and subjective, requiring considerable time and resources. Methods This study introduces a system for assessing ETI skills using video analysis. The system employs advanced video processing techniques, including a 2D convolutional autoencoder (AE) based on a self-supervision model, capable of recognizing complex patterns in videos. A 1D convolutional model enhanced with a cross-view attention module then uses AE features to make assessments. Data for the study was gathered in two phases, focusing first on comparisons between experts and novices, and then examining how novices perform under time constraints with outcomes labeled as either successful or unsuccessful. A separate set of data using videos from head-mounted cameras was also analyzed. Results The system successfully distinguishes between experts and novices in initial trials and demonstrates high accuracy in further classifications, including under time pressure and using head-mounted camera footage. Conclusions 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.https://doi.org/10.1038/s43856-025-00776-z |
| spellingShingle | Jean-Paul Ainam Erim Yanik Rahul Rahul Taylor Kunkes Lora Cavuoto Brian Clemency Kaori Tanaka Matthew Hackett Jack Norfleet Suvranu De Deep learning for video-based assessment of endotracheal intubation skills Communications Medicine |
| title | Deep learning for video-based assessment of endotracheal intubation skills |
| title_full | Deep learning for video-based assessment of endotracheal intubation skills |
| title_fullStr | Deep learning for video-based assessment of endotracheal intubation skills |
| title_full_unstemmed | Deep learning for video-based assessment of endotracheal intubation skills |
| title_short | Deep learning for video-based assessment of endotracheal intubation skills |
| title_sort | deep learning for video based assessment of endotracheal intubation skills |
| url | https://doi.org/10.1038/s43856-025-00776-z |
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