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 language | English |
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Title of host publication | Medical Imaging 2022 : Image Perception, Observer Performance, and Technology Assessment |
Editors | Claudia R. Mello-Thoms, Claudia R. Mello-Thoms, Sian Taylor-Phillips |
Number of pages | 4 |
Publisher | SPIE |
Publication date | 2022 |
Article number | 120350Y |
ISBN (Electronic) | 9781510649453 |
DOIs | |
Publication status | Published - 2022 |
Event | Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment - Virtual, Online Duration: 21 Mar 2022 → 27 Mar 2022 |
Conference
Conference | Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment |
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City | Virtual, Online |
Period | 21/03/2022 → 27/03/2022 |
Sponsor | The Society of Photo-Optical Instrumentation Engineers (SPIE) |
Series | Progress in Biomedical Optics and Imaging - Proceedings of SPIE |
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Volume | 12035 |
ISSN | 1605-7422 |
Bibliographical note
Publisher Copyright:© 2022 SPIE. All rights reserved.
Keywords
- chest
- deep learning
- eye tracking
- lung fields