ArticleFrontiers in medicine2025
Eye-tracking biomarkers of clinical expertise in ECG interpretation: statistical and machine learning evidence.
Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: Interpreting the electrocardiogram (ECG) is a fundamental clinical skill, and mistakes are still prevalent in the workforce, especially among trainees and non-specialist clinicians. Eye-tracking technology has recently become a popular method for investigating visual expertise. However, few studies have integrated visual behavior metrics with machine learning to accurately classify expertise levels. Methods: The original dataset included 62 participants from 10 healthcare roles (students, nurses, technicians, residents, fellows, consultants) who interpreted standardized ECGs. Eye movements were recorded using a Tobii Pro X2-60 tracker. ECGs were segmented into grid-based and functional Areas of Interest (AOIs). Certain eye-tracking metrics, such as Fixation Count, Time to First Fixation (TTFF), Gaze Duration, and Revisit Count, were evaluated via statistical analyses (ANOVA, Kruskal-Wallis, Results: Experts demonstrated faster TTFF, fewer revisits, and shorter fixation durations compared to novices. Experts exhibited more efficient gaze behavior, with fewer fixations within each diagnostic AOI but a higher overall fixation count per ECG due to broader systematic scanning. The correlation between fixation count and gaze duration was high ( Conclusion: Eye-tracking parameters differentiated levels of ECG interpretation expertise. These results suggest that gaze-derived metrics may serve as potential surrogate indicators that support assessment and training in medical education.
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