AI can detect signs of stroke in infants

Early intervention has the potential to improve outcomes, but it requires early detection, and that's easier said than done; the symptoms tend to be inspecific, and one routine screening method -- General Movement Assessment (GMA), which relies on recognizing movements characteristic of a stroke -- requires extensive training. Fortunately, researchers at Newcastle University and the Georgia Institute of Technology believe they've made progress toward an automated, low-cost diagnostic solution that leverages a combination of wearables and artificial intelligence (AI). Their work, which they describe in a newly published preprint paper ("Towards Reliable, Automated General Movement Assessment for Perinatal Stroke Screening in Infants Using Wearable Accelerometers"), involves outfitting newborns with body-worn sensors and applying GMA algorithms to the data collected. In small, preliminary tests involving 34 infants -- 13 of which had abnormal movements -- the researchers' system identified likely cases of a perinatal stroke over 75 percent percent of the time. "[It's an] encouraging towards our ultimate goal of an automated PS screening system that can be used population-wide," the paper's authors wrote.