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References from Diagnostic accuracy of a DenseNet-121 deep learning algorithm for chest radiograph triage in health assessment applicants: a prospective shadow-mode validation study in Nepal. Local targets link to admitted publications; unresolved targets remain external evidence.
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CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
10.1609/aaai.v33i01.3301590 · 2019 · External reference
10.1109/cvpr.2017.243
10.1109/cvpr.2017.243 · External reference
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TorchXRayVision: A library of chest X-ray datasets and models
2022 · External reference
Can we trust deep learning models diagnosis? The impact of domain shift in chest radiograph classification
2022 · External reference
Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study
10.1371/journal.pmed.1002683 · 2018 · External reference
DECIDE-AI: new reporting guidance to bridge the development-to-implementation gap in clinical artificial intelligence
2022 · External reference
STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies
10.1136/bmj.h5527 · 2015 · External reference
Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol
10.1136/bmjopen-2020-047709 · 2021 · External reference
10.1111/j.1553-2712.1996.tb03538.x
10.1111/j.1553-2712.1996.tb03538.x · External reference
10.1016/j.ejrad.2024.111783
10.1016/j.ejrad.2024.111783 · External reference
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10.1175/1520-0493(1950)078<0001:vofeit>2.0.co;2
10.1175/1520-0493(1950)078<0001:vofeit>2.0.co;2 · External reference
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10.2307/2276774
10.2307/2276774 · External reference
10.1016/0895-4356(90)90158-l
10.1016/0895-4356(90)90158-l · External reference
10.2307/2531595
10.2307/2531595 · External reference
