The use of eye tracking technology and a digital training tool in radiographic image interpretation

Abstract

This thesis investigated the efficacy of eye tracking technology and a digital training tool in radiographic image interpretation. A systematic review was performed to investigate the performance of reporting radiographers completing chest image interpretation following training. The quality of evidence published in this area was high. The role of image interpretation differed between studies, ranging between: image red dot abnormality highlighting, image comment and clinical reporting. A comparison of image interpretation skills of radiographers across a range of experience was completed using eye tracking technology. Reporting radiographers trained in MSK image interpretation demonstrated statistically significant accuracy rates (p≤0.001), and confidence levels (p≤0.001) and took a mean of 2.4 s longer to clinically decide on an image compared to students. Reporting radiographers also had a statistically greater accuracy rate (p≤0.001), were more confident (p≤0.001) and took longer to clinically decide (14 s on average) on an image diagnosis (p=0.02) than radiographers. Eye tracking patterns presented within heat maps, were a good reflection of group expertise and search strategies. Eye tracking metrics were indicative of participant performance and reflected the different search strategies that each group of participants adopted during their image interpretations. A digital training tool for use in chest image interpretation was created based on evidence within the literature, using expert input and two search strategies previously used in clinical practice. Images and diagrams, aiding translation of the tool content, were incorporated where possible. Improvements were seen in interpretation performance and confidence (p<0.05). There was a decrease in FP values and increase in TN values seen in the intervention group (p<0.05). This tool therefore has the potential to be used as a training tool in chest image interpretation for reporting clinicians and healthcare professionals. This work may contribute to improving diagnosis and help reduce reporting times.

Date of Award	Aug 2018
Original language	English
Supervisor	Sonyia Mc Fadden (Supervisor), Ciara Hughes (Supervisor) & Raymond Bond (Supervisor)