English

Improving X-ray Diagnostics through Eye-Tracking and XR

Human-Computer Interaction 2022-03-04 v1 Machine Learning

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.

Keywords

Cite

@article{arxiv.2203.01643,
  title  = {Improving X-ray Diagnostics through Eye-Tracking and XR},
  author = {Catarina Moreira and Isabel Blanco Nobre and Sandra Costa Sousa and João Madeiras Pereira and Joaquim Jorge},
  journal= {arXiv preprint arXiv:2203.01643},
  year   = {2022}
}
R2 v1 2026-06-24T10:00:38.065Z