An increasing number of algorithms can predict stroke from atrial fibrillation and characterizing plaques in carotid arteries for the same purpose. These fall under the bucket of cardiovascular or neurological diseases. Other neurological applications include assessment of acute ischemic stroke or intracranial hemorrhage. Viz.ai, a company that uses AI algorithm to do an initial read of heat CT scans to identify stroke and alert the radiologist and neurologists, became one of the first AI companies to receive insurance code and reimbursement for their technology.
MaxQ AI’s Accipio Ix in an AI workflow tool designed to help clinicians prioritize adults patients likely presenting with acute intracranial hemorrhage. Cleared by the FDA, the algorithm automatically retrieves and processes non-contrast CT images to provide a case-level indicator, which is used to triage cases most in need of expert review and diagnosis.
Another key neurological application of AI is multiple sclerosis (MS.) Multiple sclerosis is also a disease without a known cause or cure. MS is the result of the immune system attacking the myelin sheath in the brain, causing deterioration of muscle control, memory loss, and more. Google’s Verily is working with biotech company Biogen and Brigham and Women’s Hospital to set up a longitudinal study to understand how the disease develops and progresses over time. This combines data from participants wearing a Study Watch with clinical data fed into Verily’s machine learning algorithms to improve detection and understand what causes the disease to progress and flare up.
Researchers at Duke University developed an Autism & Beyond app that uses the iPhone’s front camera and facial recognition algorithms to screen children for autism. Similarly, nearly 10,000 people use the mPower app, which provides exercises like finger tapping and gait analysis to study patients with Parkinson’s disease who have consented to share their data with the broader research community.