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Diagnosing Heart Diseases with Deep Neural Networks

Stashed in: Awesome, Medicine, Turing, Heart, Machine Learning, Deep Learning

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Long and technical but fascinating first-person account of building a proof-of-concept MRI reader that is significantly faster at diagnosing heart disease than a doctor.

Sounds like their proof of concept leaves a lot of room for improvement.

Still, this is a good problem to solve:

The goal of this year’s Data Science Bowl was to estimate minimum (end-systolic) and maximum (end-diastolic) volumes of the left ventricle from a set of MRI-images taken over one heartbeat. These volumes are used by practitioners to compute an ejection fraction: fraction of outbound blood pumped from the heart with each heartbeat. This measurement can predict a wide range of cardiac problems. For a skilled cardiologist analysis of MRI scans can take up to 20 minutes, therefore, making this process automatic is obviously useful.

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