Journal article
The Use of Machine Learning in Eye Tracking Studies in Medical Imaging: A Review
IEEE journal of biomedical and health informatics, Vol.28(6), pp.3597-3612
02/29/2024
DOI: 10.1109/JBHI.2024.3371893
PMCID: PMC11262011
PMID: 38421842
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.
Details
- Title: Subtitle
- The Use of Machine Learning in Eye Tracking Studies in Medical Imaging: A Review
- Creators
- Bulat Ibragimov - University of CopenhagenClaudia Mello-Thoms - University of Iowa, Radiology
- Resource Type
- Journal article
- Publication Details
- IEEE journal of biomedical and health informatics, Vol.28(6), pp.3597-3612
- DOI
- 10.1109/JBHI.2024.3371893
- PMID
- 38421842
- PMCID
- PMC11262011
- NLM abbreviation
- IEEE J Biomed Health Inform
- eISSN
- 2168-2208
- Language
- English
- Electronic publication date
- 02/29/2024
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology
- Record Identifier
- 9984563560102771
Metrics
4 Record Views