AI to detect Covid-19 through breath sounds

December 03, 2020 // By Nick Flaherty
AI to detect Covid-19 through breath sounds
Researchers in Switzerland have used machine learning AI to identify Covid-19 from breath sounds via a digital microphone, even for asymptomatic infections, and have published the algorithms.

The DeepBreath AI deep learning algorithms developed at EPFL were originally under development for other lung conditions but have used new training data to identify patterns of Covid-19 in breath sounds

“It’s not a relaxing time to study infectious diseases,” said Dr Mary-Anne Hartley, a medical doctor and researcher in EPFL’s intelligent Global Health group (iGH). iGH is based in the Machine Learning and Optimization Laboratory of Professor Martin Jaggi, a world leading hub of AI specialists, and part of EPFL’s School of Computer and Communication Sciences. “We’ve named the new deep learning algorithms DeepChest – using lung ultrasound images – and DeepBreath – using breath sounds from a digital stethoscope. This AI is helping us to better understand complex patterns in these fundamental clinical exams. So far, results are highly promising,” said Prof Martin Jaggi.

Hartley is working with nearby Swiss university hospitals on two major projects.

At HUG, the Geneva University Hospitals, Professor Alain Gervaix has been collecting breath sounds since 2017 to build an intelligent digital stethoscope, the “Pneumoscope”. This was intended to diagnose pneumonia, and a version to be released by the end of the year should enable the diagnosis of COVID-19 from breath sounds. The first results suggest that DeepBreath is even able to detect asymptomatic COVID by identifying changes in lung tissue before the patient becomes aware of them.

“Pneumoscope with the DeepBreath algorithm can be compared to applications which can identify music based on a short sample played. The idea came from my daughter when I explained to her that auscultation allows me to hear sounds which help me identify asthma, bronchitis or pneumonia,” said Prof Gervaix.

CHUV, Lausanne’s University Hospital, is leading the clinical part of the DeepChest project, collecting thousands of lung ultrasound images from patients with Covid-19 compatible symptoms admitted to the Emergency Department.

This started in 2019 to identify markers to distinguish between viral and bacterial pneumonia but

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