Using AI-Powered Apps May Speed Up Stroke Treatment

Interesting healthcare application of AI. 

Using AI-Powered Apps May Speed Up Stroke Treatment

The Pittsburgh Post-Gazette, by Anya Sostek, March 20, 2022

Pittsburgh hospitals are using artificial intelligence (AI)-powered applications to identify patients at risk for stroke and to alert doctors. The University of Pittsburgh Medical Center (UPMC) is using the Viz.ai app to read computed tomography (CT) scans of patients and send smartphone alerts to doctors and nurses if it finds abnormalities. Meanwhile, Allegheny Health Network employs the RapidAI app for similar functions; after its introduction, the time between CT scans and artery unblocking procedures decreased from an average of 93 minutes to 68 minutes. UPMC's Raul Nogueira said the AI does not replace clinicians, but it can flag things faster and perhaps identify abnormalities a doctor might overlook. He added that Viz.ai and its underlying AI have been in development for roughly five years, while UPMC will expand its deployment from three hospitals in January to an additional two dozen through June..... ' 

Smart Helmet Assesses Strokes

Analysis that leads to treatment. 

New Smart Helmet Rapidly Assesses Stroke Patients It uses EM waves to distinguish the size, position, and type of stroke  

When someone experiences a stroke, every passing moment leading up to treatment is critical. Ideally, patients should be diagnosed and treated within the first hour, often referred to as "the Golden Hour," in order to have the best chance at recovery. Given such a tight timeline, numerous research teams have been developing portable smart helmets for diagnosing stroke in patients as they are being transported to the hospital, rather than waiting until the patient arrives at the hospital to begin testing.

Many of the smart helmet designs being explored rely on ultrasound to image the brain and detect stroke; however, this approach has several downfalls. "Ultrasound usually requires skilled personnel in order to correctly interpret the resulting images," explains Alessandro Fedeli is an assistant professor in the Department of Electrical, Electronic, Telecommunications Engineering, and Naval Architecture, at the University of Genoa. He also notes that ultrasound doesn't penetrate the skull as well as, say, electromagnetic (EM) waves.

For these reasons, his team sought to create a smart helmet that relies on EM waves, along with a signal-processing approach, to detect and diagnose stroke.  .... '