AI-based analysis of radiologist's eye movements for fatigue estimation: A pilot study on chest X-rays

Ilya Pershin, Maksim Kholiavchenko, Bulat Maksudov, Tamerlan Mustafaev, Bulat Ibragimov*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

3 Citations (Scopus)

Abstract

Radiologist-AI interaction is a novel area of research of potentially great impact. It has been observed in the literature that the radiologists' performance deteriorates towards the shift ends and there is a visual change in their gaze patterns. However, the quantitative features in these patterns that would be predictive of fatigue have not yet been discovered. A radiologist was recruited to read chest X-rays, while his eye movements were recorded. His fatigue was measured using the target concentration test and Stroop test having the number of analyzed X-rays being the reference fatigue metric. A framework with two convolutional neural networks based on UNet and ResNeXt50 architectures was developed for the segmentation of lung fields. This segmentation was used to analyze radiologist's gaze patterns. With a correlation coeffcient of 0.82, the eye gaze features extracted lung segmentation exhibited the strongest fatigue predictive powers in contrast to alternative features.

Original languageEnglish
Title of host publicationMedical Imaging 2022 : Image Perception, Observer Performance, and Technology Assessment
EditorsClaudia R. Mello-Thoms, Claudia R. Mello-Thoms, Sian Taylor-Phillips
Number of pages4
PublisherSPIE
Publication date2022
Article number120350Y
ISBN (Electronic)9781510649453
DOIs
Publication statusPublished - 2022
EventMedical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment - Virtual, Online
Duration: 21 Mar 202227 Mar 2022

Conference

ConferenceMedical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment
CityVirtual, Online
Period21/03/202227/03/2022
SponsorThe Society of Photo-Optical Instrumentation Engineers (SPIE)
SeriesProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume12035
ISSN1605-7422

Bibliographical note

Publisher Copyright:
© 2022 SPIE. All rights reserved.

Keywords

  • chest
  • deep learning
  • eye tracking
  • lung fields

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