The Use of Machine Learning in Eye Tracking Studies in Medical Imaging: A Review
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The Use of Machine Learning in Eye Tracking Studies in Medical Imaging : A Review. / Ibragimov, Bulat; Mello-Thoms, Claudia.
In: IEEE Journal of Biomedical and Health Informatics, Vol. 28, No. 6, 2024, p. 3597-3612.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - The Use of Machine Learning in Eye Tracking Studies in Medical Imaging
T2 - A Review
AU - Ibragimov, Bulat
AU - Mello-Thoms, Claudia
N1 - Publisher Copyright: Authors
PY - 2024
Y1 - 2024
N2 - Machine learning (ML) has revolutionized medical image-based diagnostics. In this review, we cover a rapidly emerging field that can be potentially significantly impacted by ML – eye tracking in medical imaging. The review investigates the clinical, algorithmic, and hardware properties of the existing studies. In particular, it evaluates 1) the type of eye-tracking equipment used and how the equipment aligns with study aims; 2) the software required to record and process eye-tracking data, which often requires user interface development, and controller command and voice recording; 3) the ML methodology utilized depending on the anatomy of interest, gaze data representation, and target clinical application. The review concludes with a summary of recommendations for future studies, and confirms that the inclusion of gaze data broadens the ML applicability in Radiology from computer-aided diagnosis (CAD) to gaze-based image annotation, physicians' error detection, fatigue recognition, and other areas of potentially high research and clinical impact.
AB - Machine learning (ML) has revolutionized medical image-based diagnostics. In this review, we cover a rapidly emerging field that can be potentially significantly impacted by ML – eye tracking in medical imaging. The review investigates the clinical, algorithmic, and hardware properties of the existing studies. In particular, it evaluates 1) the type of eye-tracking equipment used and how the equipment aligns with study aims; 2) the software required to record and process eye-tracking data, which often requires user interface development, and controller command and voice recording; 3) the ML methodology utilized depending on the anatomy of interest, gaze data representation, and target clinical application. The review concludes with a summary of recommendations for future studies, and confirms that the inclusion of gaze data broadens the ML applicability in Radiology from computer-aided diagnosis (CAD) to gaze-based image annotation, physicians' error detection, fatigue recognition, and other areas of potentially high research and clinical impact.
KW - Biomedical imaging
KW - eye tracking
KW - Gaze tracking
KW - Heating systems
KW - Machine learning
KW - machine learning
KW - Medical diagnostic imaging
KW - medical imaging
KW - Medical services
KW - radiology
KW - Reviews
KW - surgery
U2 - 10.1109/JBHI.2024.3371893
DO - 10.1109/JBHI.2024.3371893
M3 - Journal article
C2 - 38421842
AN - SCOPUS:85186993581
VL - 28
SP - 3597
EP - 3612
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
SN - 2168-2194
IS - 6
ER -
ID: 385647761