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

• Published in: IEEE journal of biomedical and health informatics Vol. 28; no. 6; pp. 3597 - 3612
• Main Authors: Ibragimov, Bulat, Mello-Thoms, Claudia
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Biomedical imaging; Diagnostic Imaging - methods; Error detection; Eye movements; Eye Movements - physiology; eye tracking; Eye-Tracking Technology; Gaze tracking; Heat maps; Humans; Image Processing, Computer-Assisted - methods; Learning algorithms; Machine Learning; Medical diagnostic imaging; Medical imaging; Radiology; Reviews; Surgery; Tracking devices; Tracking equipment
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2024.3371893

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.