Authors: John Maidens, PhD, Nicholas B Slamon, MD
Abstract/Introduction: Artificial intelligence based on deep learning has recently revolutionized diverse fields, leading to preternatural performance on perceptual tasks including image analysis and speech recognition. Deep learning has the potential to similarly transform cardiac murmur recognition through its integration with digital stethoscope technologies.
Hypothesis: A deep neural network can correctly flag pediatric heart murmurs in a noisy hospital setting with sensitivity and specificity comparable to a cardiologist.
Results: Performance of the 5 cardiologists and a ROC curve for the deep neural network are shown above. The ROC curve lies entirely below the 95%CI for 1/5 clinicians, entirely above the 95%CI for 1/5 clinicians, and within the 95%CI for 3/5 clinicians.
Conclusions: This is the first head-to-head study demonstrating that a deep neural network can detect pediatric heart murmurs with comparable accuracy to a cardiologist. Limitations include small sample size and the retrospective nature of the analysis, hence further study is justified.
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Please note: Murmur detection is part of Eko’s SENSORA™ Cardiac Disease Detection Platform. Contact our sales team to learn more.