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Development of a digital platform to aid chest radiographic image interpretation

Mclaughlin, Laura, Bond, RR, Hughes, Ciara, McConnell, Jonathan, Woznitza, Nick, Elsayed, Ayman, Cairns, Andrew, Finlay, Derwar and McFadden, Sonyia (2017) Development of a digital platform to aid chest radiographic image interpretation. In: European Congress of Radiology, Vienna. Elsevier. 3 pp. [Conference contribution]

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Abstract

Training has been provided in a variety of methods and tested for its effect on image interpretation accuracy. Studies have attempted to evaluate the effect of eye tracking based feedback/training on lung nodule detection by assessing the interpretation performance of radiographers. These researchers have provided tailored feedback using eye tracking data from the participant (expert or novice) and attempted to evaluate whether this eye-gaze based feedback had a positive impact on the participant’s performance. The feedback based on eye tracking technology proved to have a positive effect since significant improvements were found. However, no studies have been completed to test participants on their detection of a range of chest pathologies and with the involvement of training based on the eye tracking. Published guidelines and websites make recommendations about how to interpret a radiographic image. Often trainee reporting clinicians combine advice given in this guidance with a variety of recommended search techniques to form their own image interpretation search strategy. However, despite this, no optimal standardised systematic approach for chest image interpretation has been recommended using an evidence base. We are also not aware of any training tool which uses eye tracking technology to communicate effective search strategies to trainees.

Item Type:Conference contribution (Lecture)
Keywords:chest reporting, eye tracking, training tool
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Faculty of Life and Health Sciences
Faculty of Life and Health Sciences > School of Health Sciences
Research Institutes and Groups:Institute of Nursing and Health Research > Centre for Health and Rehabilitation Technologies
Institute of Nursing and Health Research
Computer Science Research Institute > Smart Environments
Computer Science Research Institute
ID Code:38641
Deposited By: Dr Sonyia McFadden
Deposited On:24 Apr 2018 12:54
Last Modified:26 Apr 2018 01:06

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