JMIR Publications

ABSTRACT

Background:

Perception-related errors comprise most diagnostic mistakes in radiology. To mitigate this problem, radiologists employ personalized and high-dimensional visual search strategies, otherwise known as search patterns. Qualitative descriptions of these search patterns, which involve the physician verbalizing or annotating the order he/she analyzes the image, can be unreliable due to discrepancies in what is reported versus the actual visual patterns. This discrepancy can interfere with quality improvement interventions and negatively impact patient care.

Objective:

The objective of this work is to provide an alternative method for distinguishing between radiologists by means of captured eye-tracking data such that the raw gaze (or processed fixation data) can be utilized to discriminate users based on subconscious behaviour in visual inspection.

Methods:

We present a novel discretized feature encoding based on spatiotemporal binning of fixation data for efficient geometric alignment and temporal ordering of eye movement when reading chest X-rays. The encoded features of the eye-fixation data are employed by machine learning classifiers to discriminate between faculty and trainee radiologists.

Results:

We include a clinical trial case study utilizing the Area Under the Curve (AUC), Accuracy, F1, Sensitivity, and Specificity metrics for class separability to evaluate the discriminability between the two subjects regarding their level of experience. We then compare the classification performance to state-of-the-art methodologies. A repeatability experiment using a separate dataset, experimental protocol, and eye tracker was also performed using eight subjects to evaluate the robustness of the proposed approach. The numerical results from both experiments demonstrate that classifiers employing the proposed feature encoding methods outperform the current state-of-the-art in differentiating between radiologists in terms of experience level. This signifies the potential impact of the proposed method for identifying radiologists’ level of expertise and those who would benefit from additional training.

Conclusions:

The effectiveness of the proposed spatio-temporal discretization approach, validated across diverse datasets and various classification metrics, underscores its potential for objective evaluation, informing targeted interventions and training strategies in radiology. This research advances reliable assessment tools, addressing challenges in perception-related errors to enhance patient care outcomes. Clinical Trial: N/A