Visual expertise in paediatric neurology

Balslev, Thomas; Jarodzka, Halszka; Holmqvist, Kenneth; Grave, Willem de; Muijtjens, Arno; Εika, Berit; Merriënboer, Jeroen Van; Scherpbier, Albert J.J.A.

doi:10.1016/j.ejpn.2011.07.004

Cited by 56 publications

(49 citation statements)

References 24 publications

(34 reference statements)

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“…For example, in a classic study on chick sexing, experts knew exactly which information was relevant for determining the sex of day-old chicks, leading to the ability to sex 98% of the chicks correct at a rate of 1000 chicks per hour, while novices did not know which information to use for discrimination between male and female chicks (Biederman & Shiffrar, 1987). In the medical domain, Balslev and colleagues found that pediatricians looked more often to task-relevant body parts (showing abnormal movements) rather than task-irrelevant body parts (showing no abnormal movements) in videos of infants having epileptic seizures, compared to medical students (Balslev et al, 2011).…”

Section: Comparison Learning For Real-life Complex Visual Tasksmentioning

confidence: 94%

Learning radiological appearances of diseases: Does comparison help?

Kok

Bruin

Robben

et al. 2013

Learning and Instruction

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a b s t r a c tComparison learning is a promising approach for learning complex real-life visual tasks. When medical students study radiological appearances of diseases, comparison of images showing diseases with images showing no abnormalities could help them learn to discriminate relevant, disease-related information. Medical students studied 12 diseases on chest x-ray images. They were randomly assigned to a group (n ¼ 31) that compared radiographs of diseases with normal images and a group (n ¼ 30) that only studied radiographs of diseases. On a visual diagnosis test, students who compared with normal images during study were better able to diagnose focal diseases (i.e., lesions at one location) than students who could not compare, but for the diagnosis of diffuse diseases (i.e., involving both lungs) there was no significant difference between groups. Results show that comparison with normal images made it easier to discriminate relevant information for focal diseases.

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Section: Comparison Learning For Real-life Complex Visual Tasksmentioning

confidence: 94%

Learning radiological appearances of diseases: Does comparison help?

Kok

Bruin

Robben

et al. 2013

Learning and Instruction

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show abstract

“…Unlike other experiments, which have contrasted experts and naïve observers [11][12][13], this is the first, to the authors' knowledge, to compare the effect of varying clinical information on visual search behaviour in radiology. In this experiment, one of the clinical conditions suggested a low expectation of abnormality (visa application), one suggested a high expectation (previous cancer) and one gave no clinical information at all suggesting an unknown expectation of abnormality.…”

Section: Introductionmentioning

confidence: 99%

Does Expectation of Abnormality Affect the Search Pattern of Radiologists When Looking for Pulmonary Nodules?

et al. 2016

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This experiment investigated whether there might be an effect on the visual search strategy of radiologists during image interpretation of the same adult chest radiographs when given different clinical information. Each of 17 experienced radiologists was asked to interpret a set of 57 (10 abnormal) posteroanterior chest images to identify the presence of pulmonary lesions using differing clinical information (leading to unknown, low and high expectations of prevalence). Eye position metrics (search time, dwell time and time to first fixation) were compared for normal and abnormal images, as well as between conditions. For all images, there was a significantly longer search time at high prevalence expectation compared to low prevalence expectation (W = 75.19, P = <0.0001). Mann-Whitney analysis of the abnormal images demonstrated that the dwell time on correctly identified lesions was significantly shorter at low prevalence expectation compared to both unknown (U = 364.5, P = 0.02) and high prevalence expectation (U = 397.0, P = 0.0002). Visual search patterns of radiologists appear to be affected by changing a priori information where such information fosters an expectation of abnormality.

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“…Verbal data, such as think-aloud data, 1 are most commonly collected together with eye-tracking data to answer this question.(e.g. 19,21,22 ) Furthermore, performance data are often a useful addition to eyetracking data. In the example of the student working with the virtual patient, retrospective thinking aloud can provide information about why the virtual tutor helped the student to find the relevant information.…”

Section: Pupil Diameter Over Timementioning

confidence: 99%

“…Theories of diagnostic reasoning suggest that less experienced students will have trouble distinguishing relevant from irrelevant information.(e.g. 19 ) How can these students be helped? Research in visual expertise shows that 'percentage of time spent on relevant information' is a measure that could be useful for investigating this phenomenon.…”

Section: Pupil Diameter Over Timementioning

confidence: 99%