# Eye-tracking biomarkers of clinical expertise in ECG interpretation: statistical and machine learning evidence

## TL;DR

Eye-tracking data can distinguish between expert and novice ECG interpreters, suggesting it could help train medical professionals.

## Contribution

This study combines eye-tracking metrics with machine learning to classify clinical expertise in ECG interpretation.

## Key findings

- Experts showed faster and more efficient gaze patterns compared to novices.
- Random Forest achieved 84% accuracy in classifying expertise levels using eye-tracking features.
- Leads V1, V2, and the rhythm strip were top predictors of expertise.

## Abstract

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.

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, t-tests). Gaze features were used to train machine learning models (Random Forest, Support Vector Machine, K-Nearest Neighbors), and clustering was performed with K-means.

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 (R2 = 0.76). Random Forest achieved the best classification accuracy (84%), outperforming SVM (78%) and KNN (74%). A Random Forest classifier achieved an accuracy of 84% using five-fold cross-validation, and performance significantly exceeded chance based on a 1,000-permutation test (p < 0.001), demonstrating robust discriminative ability. These findings indicate that gaze-based features can reliably differentiate expertise levels. The groups identified by K-means clustering corresponded (for the most part) to novice, intermediate, and expert. Feature importance showed that leads V1, V2, and the rhythm strip were the top predictors of expertise.

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.