The first FDA approval for a machine learning application to be used in a clinical setting is a big step forward for AI and machine learning in healthcare and industry as a whole.
Arterys’s medical imaging platform has been approved to be put into use to help doctors diagnose heart problems.
It uses a self-teaching artificial neural network which has learned from 1,000 cases so far, and will continue to improve its knowledge and understanding of how the heart works with each new case it examines.
Arterys was founded by Fabien Beckers, John Axerio-Cilies, Albert Hsiao and Shreyas Vasanawala when they met at Stanford University with a shared passion for the transformative potential of machine learning.
The current use for their platform – others are planned – is to help physicians understand how a heart is functioning, by providing accurate measurements of the volume of each ventricle allowing more precise assessment of health.
Beckers tells me “This is a huge deal – it’s the first time this new way of imaging has been cleared for clinical application. It’s about truly helping clinical workflow to move into the cloud and deep learning and do something pretty substantial. It opens the seals, and sets a precedent for what can be done.”
After being fed 1,000 cases as training data, Arterys Cardio DL ran supervised learning algorithms and came up with around 10 million rules based on connections it found within the data.