Ainam Jean-Paul, Yanik Erim, Rahul Rahul, Kunkes Taylor, Cavuoto Lora, Clemency Brian, Tanaka Kaori, Hackett Matthew, Norfleet Jack, De Suvranu
Center for Modeling, Simulation, & Imaging in Medicine, Rensselaer Polytechnic Institute, New York, NY, USA.
Florida Agriculture & Mechanical University-Florida State University College of Engineering, Tallahassee, FL, 32310, USA.
Commun Med (Lond). 2025 Apr 14;5(1):116. doi: 10.1038/s43856-025-00776-z.
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.
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.
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.
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.
气管插管（ETI）是在平民和战斗伤员救治环境中为建立气道而进行的一项紧急操作。医护人员熟练掌握这些技能至关重要，传统上这些技能是通过专家的直接反馈来评估的。不幸的是，这种方法可能不一致且主观，需要大量时间和资源。
本研究引入了一种使用视频分析来评估ETI技能的系统。该系统采用先进的视频处理技术，包括基于自监督模型的二维卷积自动编码器（AE），能够识别视频中的复杂模式。然后，一个通过交叉视图注意力模块增强的一维卷积模型使用AE特征进行评估。该研究的数据分两个阶段收集，首先关注专家和新手之间的比较，然后考察新手在时间限制下的表现，结果标记为成功或不成功。还分析了一组使用头戴式摄像机视频的单独数据。
该系统在初步试验中成功区分了专家和新手，并在进一步分类中表现出高精度，包括在时间压力下和使用头戴式摄像机拍摄的视频。
该系统准确区分专家和新手的能力，使其有效性以及改善医护人员培训和认证流程的潜力令人信服。
