Deep Learning in Healthcare Summit Boston

Arterys Cardio DLTM is the first technology to be cleared by the FDA that leverages cloud computing and deep learning in a clinical setting. It provides automated, editable ventricle segmentations based on conventional cardiac MRI images that are as accurate as segmentations performed manually by experienced physicians.

People who suffer a stroke often undergo a brain scan at the hospital and researchers who study the effects of strokes would love to be able to analyze these images, but the resolution is often too low for many analyses. Polina Golland &MIT Researchers have devised a way to boost the quality of these scans so they can be used for large-scale studies.

Better models of the bone, muscles and nerves that control our bodies could help doctors manage movement disorders like cerebral palsy. A new competition is crowdsourcing the search for those tools. Łukasz Kidziński dreamed up the contest as a way to better understand how people with cerebral palsy will respond to muscle-relaxing surgery.

Deep learning includes algorithms, or computer programs, that search for, identify and analyze problems without direction from people, though many humans still guide the algorithms today. Dr. Michalski has used hundreds of thousands of medical images to train a deep-learning system to detect pulmonary nodules and strokes, measure tumors and look for traumatic injury.

Google released a tool called DeepVariant that uses deep learning to identify all the mutations that an individual inherits from their parents. Engineers at Google Brain and Verily (Alphabet’s life sciences spin-off) have taught one model to take raw sequencing data and line up the billions of As, Ts, Cs, and Gs that make you you.