Deep Learning in Healthcare Summit
Boston

It’s a fact of nature that a single conversation can be interpreted in very different ways. For people with anxiety or conditions such as Asperger’s, this can make social situations extremely stressful. But what if there was a more objective way to measure and understand our interactions?

Researchers at CSAIL believe that deep learning can underpin a new generation of predictive analytics and clinical decision support tools that will safeguard patients in the intensive care unit and improve how EHRs function for decision-making.

As a doctoral candidate at Duke University, Bell developed and patented a novel signal processing technique that produces clearer ultrasound images in real time. The solution could particularly help diagnose problems in people who are obese, because fat tissue can scatter and distort ultrasound waves, delaying the detection of a serious disease.

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.

Soon, Boston-area radiologists will have AI “assistants” integrated into their daily workflows, helping them more quickly and accurately diagnose disease from MRIs, CAT scans, X-rays and more. The trained neural networks residing on DGX-1 systems in CCDS’s data center are in a constant state of learning, continually ingesting countless medical images worldwide.