ArticleAEM education and training2021
Image interpretation: Learning analytics-informed education opportunities.
Article in AEM education and training, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
5 citing papers in PubMed.
- SpacED POCUS: A Randomized Controlled Trial of an Adaptive Spaced Education POCUS Curriculum for Medical Students.POCUS journal · 2025Article
- Empowering Health Care Education Through Learning Analytics: In-depth Scoping Review.Journal of medical Internet research · 2023Article
- Twelve Tips for using Learning Curves in Health Professions Education Research.MedEdPublish (2016) · 2023Article
- Perceptual learning modules in undergraduate dermatology teaching.Clinical and experimental dermatology · 2022Article
- Image interpretation: Learning analytics-informed education opportunities.AEM education and training · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesUsing a sample of pediatric chest radiographs (pCXR) taken to rule out pneumonia, we obtained diagnostic interpretations from physicians and used learning analytics to determine the radiographic variables and participant review processes that predicted for an incorrect diagnostic interpretation.
methodsThis was a prospective cross-sectional study. A convenience sample of frontline physicians with a range of experience levels interpreted 200 pCXR presented using a customized online radiograph presentation platform. Participants were asked to determine absence or presence (with respective location) of pneumonia. The pCXR were categorized for specific image-based variables potentially associated with interpretation difficulty. We also generated heat maps displaying the locations of diagnostic error among normal pCXR. Finally, we compared image review processes in participants with higher versus lower levels of clinical experience.
resultsWe enrolled 83 participants (20 medical students, 40 postgraduate trainees, and 23 faculty) and obtained 12,178 case interpretations. Variables that predicted for increased pCXR interpretation difficulty were pneumonia versus no pneumonia (β = 8.7, 95% confidence interval [CI] = 7.4 to 10.0), low versus higher visibility of pneumonia (β = -2.2, 95% CI = -2.7 to -1.7), nonspecific lung pathology (β = 0.9, 95% CI = 0.40 to 1.5), localized versus multifocal pneumonia (β = -0.5, 95% CI = -0.8 to -0.1), and one versus two views (β = 0.9, 95% CI = 0.01 to 1.9). A review of diagnostic errors identified that bony structures, vessels in the perihilar region, peribronchial thickening, and thymus were often mistaken for pneumonia. Participants with lower experience were less accurate when they reviewed one of two available views (p < 0.0001), and accuracy of those with higher experience increased with increased confidence in their response (p < 0.0001).
conclusionsUsing learning analytics, we identified actionable learning opportunities for pCXR interpretation, which can be used to allow for a customized weighting of which cases to practice. Furthermore, experienced-novice comparisons revealed image review processes that were associated with greater diagnostic accuracy, providing additional insight into skill development of image interpretation.
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