Recognition of radiological decision errors from eye movement during chest X-ray readings
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Recognition of radiological decision errors from eye movement during chest X-ray readings. / Anikina, Anna; Pershin, Ilya; Mustafaev, Tamerlan; Ibragimov, Bulat.
Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment. ed. / Claudia R. Mello-Thoms; Claudia R. Mello-Thoms; Yan Chen. SPIE, 2024. 129290A (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, Vol. 12929).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Recognition of radiological decision errors from eye movement during chest X-ray readings
AU - Anikina, Anna
AU - Pershin, Ilya
AU - Mustafaev, Tamerlan
AU - Ibragimov, Bulat
N1 - Publisher Copyright: © 2024 SPIE.
PY - 2024
Y1 - 2024
N2 - Eye tracking in combination with artificial intelligence is a developing area of research with a wide range of applications, as evidenced by the increasing number of studies being conducted in this field. Such studies hold promising results in terms of prognosis and diagnosis, as they provide insight into how doctors interpret images and the factors that influence their decision-making processes. In this study, we investigated whether potential diagnostic errors made by physicians can be recognized through eye movements and artificial intelligence. To achieve this, we engaged four radiologists with varying levels of diagnostic experience to analyze 400 X-rays chest images with a wide range of anomalies, concurrently capturing their eye movements using an eye tracker. For each of the resulting 1546 readings, we computed numerical features extracted using radiologists’ gaze saccade data. Subsequently, we applied three machine learning algorithms such as random forest, support vector machines, k-nearest neighbor classifier, and also a neural network to map reading gaze features with radiological errors resulting in the error prediction accuracy of 0.7. Our experiments demonstrate the existence of a connection between diagnostic errors and gaze, indicating that eye-tracking data can serve as a valuable source of information for human error analysis.
AB - Eye tracking in combination with artificial intelligence is a developing area of research with a wide range of applications, as evidenced by the increasing number of studies being conducted in this field. Such studies hold promising results in terms of prognosis and diagnosis, as they provide insight into how doctors interpret images and the factors that influence their decision-making processes. In this study, we investigated whether potential diagnostic errors made by physicians can be recognized through eye movements and artificial intelligence. To achieve this, we engaged four radiologists with varying levels of diagnostic experience to analyze 400 X-rays chest images with a wide range of anomalies, concurrently capturing their eye movements using an eye tracker. For each of the resulting 1546 readings, we computed numerical features extracted using radiologists’ gaze saccade data. Subsequently, we applied three machine learning algorithms such as random forest, support vector machines, k-nearest neighbor classifier, and also a neural network to map reading gaze features with radiological errors resulting in the error prediction accuracy of 0.7. Our experiments demonstrate the existence of a connection between diagnostic errors and gaze, indicating that eye-tracking data can serve as a valuable source of information for human error analysis.
KW - artificial intelligence
KW - eye tracking
KW - x-rays images
UR - http://www.scopus.com/inward/record.url?scp=85192351102&partnerID=8YFLogxK
U2 - 10.1117/12.3006781
DO - 10.1117/12.3006781
M3 - Article in proceedings
AN - SCOPUS:85192351102
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2024
A2 - Mello-Thoms, Claudia R.
A2 - Mello-Thoms, Claudia R.
A2 - Chen, Yan
PB - SPIE
T2 - Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment
Y2 - 20 February 2024 through 22 February 2024
ER -
ID: 392146477