pivoted to Covid-19. “Many of the patients who agreed to take part in our study were scared and very ill but they wanted to contribute to broader medical research, just like we do,” said Dr Noémie Boillat-Blanco, principal investigator, I think there is a collective motivation to learn something from this crisis and to rapidly integrate new scientific knowledge into everyday medical practice.”
The algorithms have been pre-published on the EPFL website. Work is also underway to develop an application that allows these complex deep learning algorithms to work on mobile phones, even in the most remote regions. The challenge will be getting the detection engines on low cost microcontrollers, something that is focus of French developer Cartesiam.
“We want to collect data from under-represented communities so that our tools can be accurate even in poor settings,” said Hartley. “Our algorithm is for instance specifically designed to tolerate errors in image or sound collection and inconsistent quality, which are more likely in those types of settings.”
“Covid-19 has sensitized people to the vulnerability of public health, and its enormous complexity. The need to build large scale AI research efforts to understand and react to rapidly emerging data has never been more obvious. Let’s hope the momentum continues beyond the pandemic, and can be used to enable equitable access to health care,” she said.
The algorithms are avaialble at www.epfl.ch/labs/mlo/deepbreath/
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