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CHUV, EPFL, HUG | AI now sees & hears COVID in your lungs

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Deep learning algorithms automating the prediction of diagnosis & outcome of Covid-19 from chest images and breath sounds


DeepChest and DeepBreath, new deep learning algorithms developed at EPFL that identify patterns of COVID-19 in lung images and breath sounds, may help in the fight against other respiratory diseases and the growing challenge of antibiotic resistance.

For Dr Mary-Anne Hartley, a medical doctor and researcher in EPFL’s intelligent Global Health group (iGH), 2020 has been relentless. “It’s not a relaxing time to study infectious diseases,” she explained.

Since the beginning of the COVID-19 pandemic, Dr Hartley’s research team has been working non-stop with nearby Swiss university hospitals on two major projects. Using artificial intelligence (AI), they have developed new algorithms that, with data from ultrasound images and auscultation (chest/lung) sounds, can accurately diagnose the novel coronavirus in patients and predict how ill they are likely to become.

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 Professor Jaggi.


Two university hospitals involved

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. As principal investigator, Dr Noémie Boillat-Blanco explains that the project started in 2019, at first trying to identify markers that would enable better identification of viral pneumonia versus bacterial ones. However, the project took a more specific COVID focus in 2020 as many of the patients who agreed to take part in the study were scared and very ill.

At HUG, the Geneva University Hospitals, Professor Alain Gervaix, M.D., Chairman, Department of Woman, Child and Adolescent has been collecting breath sounds since 2017 to build an intelligent digital stethoscope, the “Pneumoscope”. Originally designed as a project to better diagnose pneumonia, the novel coronavirus refocused its work. The recordings have now been used to develop the DeepBreath algorithm at EPFL. Expected to be released by the end of the year it should enable the diagnosis of COVID-19 from breath sounds. Amazingly, 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.


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