Discrimination of Radiologists Utilizing Eye-Tracking Technology and
  Machine Learning: A Case Study
Stanford Martinez Carolina Ramirez-Tamayo Syed Hasib Akhter Faruqui Kal L. Clark Adel Alaeddini Nicholas Czarnek Aarushi Aggarwal Sahra Emamzadeh Jeffrey R. Mock Edward J. Golob
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. This study presents 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. 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 in regard to 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.