AI-based analysis of radiologist's eye movements for fatigue estimation: A pilot study on chest X-rays
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AI-based analysis of radiologist's eye movements for fatigue estimation : A pilot study on chest X-rays. / Pershin, Ilya; Kholiavchenko, Maksim; Maksudov, Bulat; Mustafaev, Tamerlan; Ibragimov, Bulat.
Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment. ed. / Claudia R. Mello-Thoms; Claudia R. Mello-Thoms; Sian Taylor-Phillips. SPIE, 2022. 120350Y (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, Vol. 12035).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - AI-based analysis of radiologist's eye movements for fatigue estimation
T2 - Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment
AU - Pershin, Ilya
AU - Kholiavchenko, Maksim
AU - Maksudov, Bulat
AU - Mustafaev, Tamerlan
AU - Ibragimov, Bulat
N1 - Publisher Copyright: © 2022 SPIE. All rights reserved.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - chest
KW - deep learning
KW - eye tracking
KW - lung fields
U2 - 10.1117/12.2612760
DO - 10.1117/12.2612760
M3 - Article in proceedings
AN - SCOPUS:85131881206
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2022
A2 - Mello-Thoms, Claudia R.
A2 - Mello-Thoms, Claudia R.
A2 - Taylor-Phillips, Sian
PB - SPIE
Y2 - 21 March 2022 through 27 March 2022
ER -
ID: 344726142