The Use of Machine Learning in Eye Tracking Studies in Medical Imaging: A Review
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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.
Original language | English |
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Journal | IEEE Journal of Biomedical and Health Informatics |
Number of pages | 19 |
ISSN | 2168-2194 |
DOIs | |
Publication status | E-pub ahead of print - 2024 |
Bibliographical note
Publisher Copyright:
Authors
- Biomedical imaging, eye tracking, Gaze tracking, Heating systems, Machine learning, machine learning, Medical diagnostic imaging, medical imaging, Medical services, radiology, Reviews, surgery
Research areas
ID: 385647761