Prediction of radiological decision errors from longitudinal analysis of gaze and image features

Medical imaging, particularly radiography, is an indispensable part of diagnosing many chest diseases. Final diagnoses are made by radiologists based on images, but the decision-making process is always associated with a risk of incorrect interpretation. Incorrectly interpreted data can lead to delays in treatment, a prescription of inappropriate therapy, or even a completely missed diagnosis. In this context, our study aims to determine whether it is possible to predict diagnostic errors made by radiologists using eye-tracking technology. For this purpose, we asked 4 radiologists with different levels of experience to analyze 1000 images covering a wide range of chest diseases. Using eye-tracking data, we calculated the radiologists’ gaze fixation points and generated feature vectors based on this data to describe the radiologists’ gaze behavior during image analysis. Additionally, we emulated the process of revealing the read images following radiologists’ gaze data to create a more comprehensive picture of their analysis. Then we applied a recurrent neural network to predict diagnostic errors. Our results showed a 0.7755 ROC AUC score, demonstrating a significant potential for this approach in enhancing the accuracy of diagnostic error recognition. •Investigated radiologists’ error detection for longitudinal eye movements.•Proposed an AI framework mimicking human gaze for image reading.•Validated AI framework on 4000 X-ray readings by four radiologists.•Used AI framework to identify patterns linked to radiologist errors.