Sign up FAST! Login

Diagnosing Heart Diseases with Deep Neural Networks


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

To save this post, select a stash from drop-down menu or type in a new one:

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.

You May Also Like: