Improving X-ray Diagnostics through Eye-Tracking and XR

TL;DR

This paper explores how combining eye-tracking, virtual reality, and machine learning can improve the efficiency and accuracy of X-ray diagnostics by addressing ergonomic and environmental challenges faced by radiologists.

Contribution

It introduces a novel approach integrating eye-tracking with VR and machine learning to enhance diagnostic accuracy and workflow in X-ray reading.

Findings

Potential to reduce diagnostic errors

Improved reading comfort and ergonomics

Enhanced diagnostic speed and accuracy

Abstract

There is a growing need to assist radiologists in performing X-ray readings and diagnoses fast, comfortably, and effectively. As radiologists strive to maximize productivity, it is essential to consider the impact of reading rooms in interpreting complex examinations and ensure that higher volume and reporting speeds do not compromise patient outcomes. Virtual Reality (VR) is a disruptive technology for clinical practice in assessing X-ray images. We argue that conjugating eye-tracking with VR devices and Machine Learning may overcome obstacles posed by inadequate ergonomic postures and poor room conditions that often cause erroneous diagnostics when professionals examine digital images.