MIT Team’s Cough Detector Identifies 97% of COVID-19 Cases Even in Asymptomatic People

Part of the challenge in controlling the coronavirus pandemic is in identifying and isolating infected people quickly – not particularly easy when COVID-19 symptoms aren’t always noticeable, especially early on. Now scientists have developed a new artificial intelligence model that can detect the virus from a simple forced cough.

Evidence shows that the AI can spot differences in coughing that can’t be heard with the human ear, and if the detection system can be incorporated into a device like a smartphone, the research team thinks it could become a useful early screening tool.

The work builds on research that was already happening into Alzheimer’s detection through coughing and talking. Once the pandemic started to spread, the team turned its attention to COVID-19 instead, tapping into what had already been learned about how disease can cause very small changes to speech and the other noises we make.

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Committee begins review of High Priority Projects free software list

The High Priority Free Software Projects (HPP) initiative draws attention to areas of improvement to the HPP list and specific projects of great strategic importance to the goal of freedom for all computer users. Longtime committee member Benjamin Mako Hill said previously that an “updated High Priority Projects list is a description of the most important threats, and most critical opportunities, that free software faces in the modern computing landscape.” As computing is more ubiquitous than ever, the HPP list must reflect ongoing changes in priorities for the free software movement. The committee is starting the new process of updating the HPP, and we need your input.

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Read about What EEGs tell us about COVID-19 and the brain here

Throughout the pandemic, healthcare workers have seen more than just the lungs affected by COVID-19. Doctors have reported neurological complications including stroke, headache and seizures, but the information is limited to a number of individual reports that are not reflective of a larger population.

Researchers from Baylor College of Medicine and the University of Pittsburgh have gathered more than 80 studies, reviewed the data, and identified commonalities that are helping to paint a broader picture of how COVID-19 affects the brain.

The findings, published in Seizure: European Journal of Epilepsy, focused on electroencephalogram (EEG) abnormalities of the brain. EEG is a test used to evaluate the electrical activity in the brain. Researchers found that about one-third of patients who were given an EEG had abnormal neuroimaging localized in the frontal lobe of the brain.

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The Apple Watch heart monitor sends too many people to the doctor

Only a handful of people the watch flagged actually had a heart problem

The heart monitoring feature on the Apple Watch may lead to unnecessary health care visits, according to a new study published this week. Only around 10 percent of people who saw a doctor at the Mayo Clinic after noticing an abnormal pulse reading on their watch were eventually diagnosed with a cardiac condition.

The finding shows that at-home health monitoring devices can lead to over-utilization of the health care system, said study author Heather Heaton, an assistant professor of emergency medicine at the Mayo Clinic College of Medicine, in an email to The Verge. That may be expensive for patients and for the system as a whole, and it may take up doctor and patient time unnecessarily.

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