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Briefing
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- Arrhythmia Diagnosis – Stanford researchers developed deep learning algorithm that can diagnose 14 types of arrhythmias, or irregular heart beat defects, from wearables data
- Neural Network Training – Trained neural network with 30,000 30-second heart beat electrocardiogram (ECG) clips from patients with different types of arrhythmias
- Test Results – Algorithm detected arrhythmias better than individual expert cardiologists in 300 undiagnosed clips
- Potential Applications – Could provide instantaneous arrhythmia diagnosis for patients in rural areas with insufficient cardiologists, improving quality of care and saving doctors time
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Accelerator
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Sector
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Healthcare/Health Sciences, Information Technology
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Organization
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Stanford University
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Source
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Original Publication Date
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July 6, 2017
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