Key result
A deep learning model based on convolutional neural networks achieved an accuracy of 0.767 in detecting chronic kidney disease from electrocardiograms in a primary cohort.
Why the study?
Does a deep learning model applied to ECG waveforms accurately detect chronic kidney disease?
Does a deep learning model applied to ECG waveforms accurately detect chronic kidney disease?
Answers are grounded in this paper and cite their sources.
Deep learning models applied to routine ECGs show promise for detecting chronic kidney disease, though further validation is needed before widespread clinical implementation.
Although chronic kidney disease (CKD) can be detected by relatively simple blood and urine tests, it remains underdiagnosed even in patients at high risk for the disease. Deep learning-based approaches are now being developed to detect CKD based on electrocardiogram (ECG) data. Kooman outlines the findings of a Communications Medicine article on deep learning-based detection of chronic kidney disease using ECGs. The author interprets the study’s findings in light of its limitations and outlines key considerations in the clinical implementation of such an approach.
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Jeroen P. Kooman (2023) conducted an editorial in Chronic kidney disease (n=111,370). Deep learning model for ECG analysis was evaluated on Accuracy of detecting CKD. A deep learning model based on convolutional neural networks achieved an accuracy of 0.767 in detecting chronic kidney disease from electrocardiograms in a primary cohort.
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