# Deep Learning for Video-Based Assessment of Endotracheal Intubation Skills

arXiv: https://arxiv.org/abs/2404.11727 · HTML: https://mlsanity.com/paper/2404.11727/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 41 references that are in the map.
- First posted on arXiv 17 Apr 2024.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Multi-view Convolutional Neural Networks for 3D Shape Recognition](https://mlsanity.com/paper/1505.00880/) (2015)
- [Adversarial Autoencoders](https://mlsanity.com/paper/1511.05644/) (2015)
- [Hierarchical Question-Image Co-Attention for Visual Question Answering](https://mlsanity.com/paper/1606.00061/) (2016)
- [Stacked Attention Networks for Image Question Answering](https://mlsanity.com/paper/1511.02274/) (2015)
- [Multimodal Deep Learning](https://mlsanity.com/paper/2301.04856/) (2023)
- [On Deep Multi-View Representation Learning: Objectives and Optimization](https://mlsanity.com/paper/1602.01024/) (2016)
- [Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering](https://mlsanity.com/paper/1511.05234/) (2015)
- [Video-based surgical skill assessment using 3D convolutional neural networks](https://mlsanity.com/paper/1903.02306/) (2019)
- [It Takes (Only) Two: Adversarial Generator-Encoder Networks](https://mlsanity.com/paper/1704.02304/) (2017)
- [How Much Can We Really Trust You? Towards Simple, Interpretable Trust Quantification Metrics for Deep Neural Networks](https://mlsanity.com/paper/2009.05835/) (2020)
- [Video-based Formative and Summative Assessment of Surgical Tasks using Deep Learning](https://mlsanity.com/paper/2203.09589/) (2022)
- [Where Does Trust Break Down? A Quantitative Trust Analysis of Deep Neural Networks via Trust Matrix and Conditional Trust Densities](https://mlsanity.com/paper/2009.14701/) (2020)
- [One-shot skill assessment in high-stakes domains with limited data via meta learning](https://mlsanity.com/paper/2301.00812/) (2022)

## Abstract (arXiv)

Endotracheal intubation (ETI) is an emergency procedure performed in civilian and combat casualty care settings to establish an airway. Objective and automated assessment of ETI skills is essential for the training and certification of healthcare providers. However, the current approach is based on manual feedback by an expert, which is subjective, time- and resource-intensive, and is prone to poor inter-rater reliability and halo effects. This work proposes a framework to evaluate ETI skills using single and multi-view videos. The framework consists of two stages. First, a 2D convolutional autoencoder (AE) and a pre-trained self-supervision network extract features from videos. Second, a 1D convolutional enhanced with a cross-view attention module takes the features from the AE as input and outputs predictions for skill evaluation. The ETI datasets were collected in two phases. In the first phase, ETI is performed by two subject cohorts: Experts and Novices. In the second phase, novice subjects perform ETI under time pressure, and the outcome is either Successful or Unsuccessful. A third dataset of videos from a single head-mounted camera for Experts and Novices is also analyzed. The study achieved an accuracy of 100% in identifying Expert/Novice trials in the initial phase. In the second phase, the model showed 85% accuracy in classifying Successful/Unsuccessful procedures. Using head-mounted cameras alone, the model showed a 96% accuracy on Expert and Novice classification while maintaining an accuracy of 85% on classifying successful and unsuccessful. In addition, GradCAMs are presented to explain the differences between Expert and Novice behavior and Successful and Unsuccessful trials. The approach offers a reliable and objective method for automated assessment of ETI skills.