Thirkettle Martin, Thyoka Mandela, Gopalan Padmini, Fernandes Nadiah, Stafford Tom, Offiah Amaka C
1 Department of Psychology, Sociology & Politics, Sheffield Hallam University , Sheffield , United Kingdom.
2 Department of Radiology, Sheffield Teaching Hospitals , Sheffield , United Kingdom.
Br J Radiol. 2019 May;92(1097):20180958. doi: 10.1259/bjr.20180958. Epub 2019 Feb 28.
Expert radiologists exhibit high levels of visual diagnostic accuracy from review of radiological images, doing so after accumulating years of training and experience. To train new radiologists, learning interventions must focus on the development of these skills. By developing a web-based measure of image assessment, a key part of visual diagnosis, we aimed to capture differences in the performance of expert, trainee and non-radiologists.
12 consultant paediatric radiologists, 12 radiology registrars, and 39 medical students were recruited to the study. All participants completed a two-part, online task requiring them to visually assess 30 images (25 containing an abnormality) drawn from a library of 150 paediatric skeletal radiographs assessed prior to the study. Participants first identified whether an image contained an abnormality, and then clicked within the image to mark its location. Performance measures of identification accuracy, localisation precision, and task time were collected.
Despite the difficulties of web-based testing, large differences in performance, both in terms of the accuracy of abnormality identification and in the precision of abnormality localisation were found between groups, with consultant radiologists the most accurate both at identifying images containing abnormalities ( < 0.001) and at localising abnormalities on the images ( < 0.001).
Our data demonstrate that an online measurement of radiological skill is sufficiently sensitive to detect group level changes in performance consistent with the development of expertise.
The developed tool will allow future studies assessing the impact of different training strategies on cognitive performance and diagnostic accuracy.
专业放射科医生在积累多年培训和经验后,通过审查放射影像表现出高水平的视觉诊断准确性。为了培训新的放射科医生,学习干预措施必须专注于这些技能的培养。通过开发一种基于网络的图像评估方法(视觉诊断的关键部分),我们旨在捕捉专家、实习生和非放射科医生在表现上的差异。
招募了12名儿科放射科顾问医生、12名放射科住院医生和39名医学生参与该研究。所有参与者完成了一项分为两部分的在线任务,要求他们对从150张儿科骨骼X光片库中抽取的30张图像(25张包含异常)进行视觉评估,这些X光片在研究前已进行过评估。参与者首先识别图像是否包含异常,然后在图像内点击以标记其位置。收集了识别准确性、定位精度和任务时间等表现指标。
尽管基于网络测试存在困难,但在异常识别准确性和异常定位精度方面,不同组之间在表现上存在很大差异,放射科顾问医生在识别包含异常的图像(<0.001)和在图像上定位异常(<0.001)方面最为准确。
我们的数据表明,放射学技能的在线测量对检测与专业技能发展一致的组水平表现变化具有足够的敏感性。
所开发的工具将使未来的研究能够评估不同培训策略对认知表现和诊断准确性的影响。