New “Sight and Sound” Algorithms to Identify COVID-19 Patterns

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About a year ago, when the novel coronavirus broke out, medical science not only failed to arrest its spread but also to properly identify the developmental stages of the disease. Many casualties resulted because the progression of the disease was an enigma. In the later part of the year, there were nascent attempts to harness AI for COVID-19 diagnosis, treatment, and monitoring. A giant step in that direction has been taken recently by researchers at EPFL; they have developed algorithms that can practically see and hear COVID in a patient’s lungs.

The new deep learning algorithms DeepChest and DeepBreath have been developed by the team of Dr. Mary-Anne Hartley at EPFL’s intelligent Global Health group (iGH) based in the Machine Learning and Optimization Laboratory of Professor Martin Jaggi. DeepChest uses lung ultrasound images, while DeepBreath utilizes breath sounds from a digital stethoscope. The algorithms can accurately diagnose the novel coronavirus in patients and predict how much they will be affected by the virus strain.

Two nearby Swiss university hospitals are involved in developing the algorithms. At HUG, the Geneva University Hospitals, Professor Alain Gervaix has been collecting breath sounds since 2017 to develop an intelligent digital stethoscope called the “Pneumoscope” to diagnose pneumonia. The recordings have now helped develop the DeepBreath algorithm at EPFL. Initial trials show that DeepBreath can detect even asymptomatic COVID by identifying changes in lung tissue before the patient becomes aware of them.

The clinical aspects of the DeepChest project are being conducted at CHUV, Lausanne’s University Hospital. Thousands of lung ultrasound images are being collected from patients admitted to the Emergency Department with COVID-19 symptoms. Although the image sample collection process started last year, they have since focused on COVID-19.

The algorithms are available on the EPFL website, but they are very much a work in progress. Efforts are on to further refine and validate the algorithms by inviting coding skills from around the world, including a year-long hackathon called ‘CODED-19’ by the EPFL community. As Professor Jaggi explains, “This AI is helping us to better understand complex patterns in these fundamental clinical exams. So far, results are highly promising.” At a later stage, iGH plans to develop a mobile application for the deep learning algorithms and make them available far and wide.

While the current effort is to specifically meet the COVID-19 challenge, its preliminary success has amply demonstrated how large-scale AI research can be used to remove some of the roadblocks for medical science.

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