Jean-Paul Ainam, Rahul, Lora Cavuoto, Matthew Hackett, Jack Norfleet, Suvranu De
This paper introduces a machine learning approach using human gaze data to objectively assess airway management skills, improving accuracy and efficiency over traditional methods.
This research focuses on improving how medical professionals are trained to perform critical airway management procedures, like endotracheal intubation, which are vital in emergencies. The traditional way of assessing these skills is often subjective and may not reflect real-world competence. By using a machine learning system that analyzes where people look during these procedures, the researchers have developed a more accurate and objective way to assess these skills. This new approach not only enhances training but also helps ensure better patient outcomes, especially in high-pressure environments like military settings.
