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

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

Standard

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. red. / Claudia R. Mello-Thoms; Claudia R. Mello-Thoms; Sian Taylor-Phillips. SPIE, 2022. 120350Y (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, Bind 12035).

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

Harvard

Pershin, I, Kholiavchenko, M, Maksudov, B, Mustafaev, T & Ibragimov, B 2022, AI-based analysis of radiologist's eye movements for fatigue estimation: A pilot study on chest X-rays. i CR Mello-Thoms, CR Mello-Thoms & S Taylor-Phillips (red), Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment., 120350Y, SPIE, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, bind 12035, Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment, Virtual, Online, 21/03/2022. https://doi.org/10.1117/12.2612760

APA

Pershin, I., Kholiavchenko, M., Maksudov, B., Mustafaev, T., & Ibragimov, B. (2022). AI-based analysis of radiologist's eye movements for fatigue estimation: A pilot study on chest X-rays. I C. R. Mello-Thoms, C. R. Mello-Thoms, & S. Taylor-Phillips (red.), Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment [120350Y] SPIE. Progress in Biomedical Optics and Imaging - Proceedings of SPIE Bind 12035 https://doi.org/10.1117/12.2612760

Vancouver

Pershin I, Kholiavchenko M, Maksudov B, Mustafaev T, Ibragimov B. AI-based analysis of radiologist's eye movements for fatigue estimation: A pilot study on chest X-rays. I Mello-Thoms CR, Mello-Thoms CR, Taylor-Phillips S, red., Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment. SPIE. 2022. 120350Y. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, Bind 12035). https://doi.org/10.1117/12.2612760

Author

Pershin, Ilya ; Kholiavchenko, Maksim ; Maksudov, Bulat ; Mustafaev, Tamerlan ; Ibragimov, Bulat. / AI-based analysis of radiologist's eye movements for fatigue estimation : A pilot study on chest X-rays. Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment. red. / Claudia R. Mello-Thoms ; Claudia R. Mello-Thoms ; Sian Taylor-Phillips. SPIE, 2022. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, Bind 12035).

Bibtex

@inproceedings{7d2542f2d56842c2ae18ac86b50ab4b6,
title = "AI-based analysis of radiologist's eye movements for fatigue estimation: A pilot study on chest X-rays",
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.",
keywords = "chest, deep learning, eye tracking, lung fields",
author = "Ilya Pershin and Maksim Kholiavchenko and Bulat Maksudov and Tamerlan Mustafaev and Bulat Ibragimov",
note = "Publisher Copyright: {\textcopyright} 2022 SPIE. All rights reserved.; Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment ; Conference date: 21-03-2022 Through 27-03-2022",
year = "2022",
doi = "10.1117/12.2612760",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Mello-Thoms, {Claudia R.} and Mello-Thoms, {Claudia R.} and Sian Taylor-Phillips",
booktitle = "Medical Imaging 2022",
address = "United States",

}

RIS

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