Airway Skill Assessment with Spatiotemporal Attention Mechanisms Using Human Gaze

• URL: http://arxiv.org/abs/2506.19306v1
• Date: Tue, 24 Jun 2025 04:40:58 GMT
• Title: Airway Skill Assessment with Spatiotemporal Attention Mechanisms Using Human Gaze
• Authors: Jean-Paul Ainam, Rahul, Lora Cavuoto, Matthew Hackett, Jack Norfleet, Suvranu De,
• Abstract summary: Airway management skills are critical in emergency medicine and are typically assessed through subjective evaluation.<n>This paper proposes a machine learning-based approach for assessing airway skills, specifically endotracheal intubation (ETI)<n>The proposed system leverages an attention mechanism guided by the human gaze to enhance the recognition of successful and unsuccessful ETI procedures.
• Score: 2.125763116614213
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Airway management skills are critical in emergency medicine and are typically assessed through subjective evaluation, often failing to gauge competency in real-world scenarios. This paper proposes a machine learning-based approach for assessing airway skills, specifically endotracheal intubation (ETI), using human gaze data and video recordings. The proposed system leverages an attention mechanism guided by the human gaze to enhance the recognition of successful and unsuccessful ETI procedures. Visual masks were created from gaze points to guide the model in focusing on task-relevant areas, reducing irrelevant features. An autoencoder network extracts features from the videos, while an attention module generates attention from the visual masks, and a classifier outputs a classification score. This method, the first to use human gaze for ETI, demonstrates improved accuracy and efficiency over traditional methods. The integration of human gaze data not only enhances model performance but also offers a robust, objective assessment tool for clinical skills, particularly in high-stress environments such as military settings. The results show improvements in prediction accuracy, sensitivity, and trustworthiness, highlighting the potential for this approach to improve clinical training and patient outcomes in emergency medicine.

Related papers

• Human Scanpath Prediction in Target-Present Visual Search with Semantic-Foveal Bayesian Attention [49.99728312519117]
  SemBA-FAST is a top-down framework designed for predicting human visual attention in target-present visual search.<n>We evaluate SemBA-FAST on the COCO-Search18 benchmark dataset, comparing its performance against other scanpath prediction models.<n>These findings provide valuable insights into the capabilities of semantic-foveal probabilistic frameworks for human-like attention modelling.
  arXiv  Detail & Related papers  (2025-07-24T15:19:23Z)
• Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks [1.0889037375410424]
  Human action recognition plays a critical role in healthcare and medicine, supporting applications such as patient behavior monitoring, fall detection, surgical robot supervision, and procedural skill assessment.<n>Recent advancements in vision-language models, especially the transformer-based CLIP model, offer promising capabilities for generalizing action recognition from video data.
  arXiv  Detail & Related papers  (2025-07-24T13:13:28Z)
• Dynamic directed functional connectivity as a neural biomarker for objective motor skill assessment [1.1993517339149389]
  This study proposes a novel method for motor skill assessment based on dynamic directed functional connectivity (dFC) as a neural biomarker.<n>Electroencephalography (EEG) to capture brain dynamics and employing an attention-based Long Short-Term Memory (LSTM) model for non-linear Granger causality analysis.<n>Convolutional neural network (CNN) is then used to classify skill levels, achieving greater accuracy and specificity than established performance metrics in laparoscopic surgery.
  arXiv  Detail & Related papers  (2025-02-19T01:51:39Z)
• Multi-Modal Self-Supervised Learning for Surgical Feedback Effectiveness Assessment [66.6041949490137]
  We propose a method that integrates information from transcribed verbal feedback and corresponding surgical video to predict feedback effectiveness. Our findings show that both transcribed feedback and surgical video are individually predictive of trainee behavior changes. Our results demonstrate the potential of multi-modal learning to advance the automated assessment of surgical feedback.
  arXiv  Detail & Related papers  (2024-11-17T00:13:00Z)
• EEG Emotion Copilot: Optimizing Lightweight LLMs for Emotional EEG Interpretation with Assisted Medical Record Generation [12.707059419820848]
  This paper presents the EEG Emotion Copilot, which first recognizes emotional states directly from EEG signals.<n>It then generates personalized diagnostic and treatment suggestions, and finally supports the automation of assisted electronic medical records.<n>The proposed copilot is expected to advance the application of affective computing in the medical domain.
  arXiv  Detail & Related papers  (2024-09-30T19:15:05Z)
• Cognitive-Motor Integration in Assessing Bimanual Motor Skills [0.0]
  This study introduces a novel approach by leveraging deep neural networks (DNNs) to analyze and integrate both cognitive decision-making and motor execution. We tested this methodology by assessing laparoscopic surgery skills within the Fundamentals of Laparoscopic Surgery program.
  arXiv  Detail & Related papers  (2024-04-16T20:20:23Z)
• Emotic Masked Autoencoder with Attention Fusion for Facial Expression Recognition [1.4374467687356276]
  This paper presents an innovative approach integrating the MAE-Face self-supervised learning (SSL) method and multi-view Fusion Attention mechanism for expression classification. We suggest easy-to-implement and no-training frameworks aimed at highlighting key facial features to determine if such features can serve as guides for the model. The efficacy of this method is validated by improvements in model performance on the Aff-wild2 dataset.
  arXiv  Detail & Related papers  (2024-03-19T16:21:47Z)
• Dissecting Self-Supervised Learning Methods for Surgical Computer Vision [51.370873913181605]
  Self-Supervised Learning (SSL) methods have begun to gain traction in the general computer vision community. The effectiveness of SSL methods in more complex and impactful domains, such as medicine and surgery, remains limited and unexplored. We present an extensive analysis of the performance of these methods on the Cholec80 dataset for two fundamental and popular tasks in surgical context understanding, phase recognition and tool presence detection.
  arXiv  Detail & Related papers  (2022-07-01T14:17:11Z)
• Leveraging Human Selective Attention for Medical Image Analysis with Limited Training Data [72.1187887376849]
  The selective attention mechanism helps the cognition system focus on task-relevant visual clues by ignoring the presence of distractors. We propose a framework to leverage gaze for medical image analysis tasks with small training data. Our method is demonstrated to achieve superior performance on both 3D tumor segmentation and 2D chest X-ray classification tasks.
  arXiv  Detail & Related papers  (2021-12-02T07:55:25Z)
• Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification [101.49122450005869]
  We present a counterfactual attention learning method to learn more effective attention based on causal inference. Specifically, we analyze the effect of the learned visual attention on network prediction. We evaluate our method on a wide range of fine-grained recognition tasks.
  arXiv  Detail & Related papers  (2021-08-19T14:53:40Z)
• Continuous Emotion Recognition via Deep Convolutional Autoencoder and Support Vector Regressor [70.2226417364135]
  It is crucial that the machine should be able to recognize the emotional state of the user with high accuracy. Deep neural networks have been used with great success in recognizing emotions. We present a new model for continuous emotion recognition based on facial expression recognition.
  arXiv  Detail & Related papers  (2020-01-31T17:47:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.