MedTech Startup Viz Diagnoses Strokes with Machine Learning

Strokes are an alarmingly common problem in America, but they are rarely handled properly, with only three out of every 100 stroke victims receiving the right care. Strokes strike fast and are tricky to diagnose, and because they are so difficult to identify and treat, they often leave the patients lucky enough to survive with devastating after-effects, including impaired motor skills, vision problems, speech and communication issues, and emotional and behavioral changes. But as MedTech app developers prove everyday, many health issues, including strokes, can be approached from a new angle. By utilizing the most sophisticated and up-to-date technology, we can save precious time, money, and lives.

San Francisco-based MedTech startup Viz is taking an innovative approach toward the treatment of strokes that should excite mHealth app developers. The startup combines the expertise of its two founders, neurosurgeon Chris Mansi and statistician and deep learning engineer David Golan, to apply AI to more quickly and effectively diagnose neurological conditions such as strokes. The vast majority of ER doctors lack expertise in neuroscience, so Mansi and Golan hope to provide them with the knowledge of the world’s top neuroscientists via machine learning. With Viz’s AI analyzing brain scans, the obscure indicators that a stroke has occurred will no longer be overlooked — and if doctors can identify a stroke, they can better treat one.

The startup has received $2.5 million in backing from the investment firms of former Google exec Eric Schmidt and Yahoo founder Jerry Yang. It’s easy to see why Viz would create such a stir. Unidentified or improperly treated strokes don’t just ruin lives — they put a $74 billion dollar strain on the American healthcare system. App developers have long championed MedTech solutions for their life-saving potential as well as their cost-effectiveness. Companies like Viz are our best hope for fixing the broken American healthcare system.