The Use of Machine Learning in Eye Tracking Studies in Medical Imaging: A Review

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Standard

The Use of Machine Learning in Eye Tracking Studies in Medical Imaging : A Review. / Ibragimov, Bulat; Mello-Thoms, Claudia.

I: IEEE Journal of Biomedical and Health Informatics, 2024.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Ibragimov, B & Mello-Thoms, C 2024, 'The Use of Machine Learning in Eye Tracking Studies in Medical Imaging: A Review', IEEE Journal of Biomedical and Health Informatics. https://doi.org/10.1109/JBHI.2024.3371893

APA

Ibragimov, B., & Mello-Thoms, C. (2024). The Use of Machine Learning in Eye Tracking Studies in Medical Imaging: A Review. IEEE Journal of Biomedical and Health Informatics. https://doi.org/10.1109/JBHI.2024.3371893

Vancouver

Ibragimov B, Mello-Thoms C. The Use of Machine Learning in Eye Tracking Studies in Medical Imaging: A Review. IEEE Journal of Biomedical and Health Informatics. 2024. https://doi.org/10.1109/JBHI.2024.3371893

Author

Ibragimov, Bulat ; Mello-Thoms, Claudia. / The Use of Machine Learning in Eye Tracking Studies in Medical Imaging : A Review. I: IEEE Journal of Biomedical and Health Informatics. 2024.

Bibtex

@article{712db9d59bbb4daeb9192e393402d090,
title = "The Use of Machine Learning in Eye Tracking Studies in Medical Imaging: A Review",
abstract = "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.",
keywords = "Biomedical imaging, eye tracking, Gaze tracking, Heating systems, Machine learning, machine learning, Medical diagnostic imaging, medical imaging, Medical services, radiology, Reviews, surgery",
author = "Bulat Ibragimov and Claudia Mello-Thoms",
note = "Publisher Copyright: Authors",
year = "2024",
doi = "10.1109/JBHI.2024.3371893",
language = "English",
journal = "IEEE Journal of Biomedical and Health Informatics",
issn = "2168-2194",
publisher = "Institute of Electrical and Electronics Engineers",

}

RIS

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

UR - http://www.scopus.com/inward/record.url?scp=85186993581&partnerID=8YFLogxK

U2 - 10.1109/JBHI.2024.3371893

DO - 10.1109/JBHI.2024.3371893

M3 - Journal article

C2 - 38421842

AN - SCOPUS:85186993581

JO - IEEE Journal of Biomedical and Health Informatics

JF - IEEE Journal of Biomedical and Health Informatics

SN - 2168-2194

ER -

ID: 385647761