Open-access AI invites refinement of COVID-19 diagnosis

Researchers at the University of Waterloo in Ontario have introduced a convolutional neural network for diagnosing COVID-19 on chest X-rays.

Other research teams have announced similar AI tools in recent weeks, but this one is publicly available to AI developers who wish to explore, improve or otherwise noodle around with its inner workings.

Called COVID-Net, the network was developed by Alexander Wong, P.Eng, PhD, who is also co-founder and chief scientist of the Canadian AI company DarwinAI, and Linda Wang, a U of Waterloo graduate student researcher.

They trained the system on 5,941 chest radiographs acquired from 2,839 patients whose imaging data is available in two open-access data repositories. They’re calling the resulting dataset COVIDx.

The researchers’ initial results showed the system “strikes a good balance between accuracy and computational complexity,” as it achieved 83.5% test accuracy on the 2.26 billion decisions it needed to make its diagnostic predictions.

In their discussion section, the authors underscore that the system is not ready for prime time—hence the decision to open-source it.

Their stated hope is that “the promising results achieved by COVID-Net on the COVIDx test dataset, along with the fact that it is available in open-source format alongside the description on constructing the open source dataset, will lead it to be leveraged and built upon by both researchers and citizen data scientists alike.”  

They add that their aim is to “accelerate the development of highly accurate yet practical deep-learning solutions for detecting COVID-19 cases from chest radiography images” while also speeding treatment to patients.

Importantly, Wong and Wang note their use of an explainable AI method, which yields transparency and so may ward off clinicians’ wariness of “black box” computing.

Their research report is available in full for free.

Dave Pearson

Dave P. has worked in journalism, marketing and public relations for more than 30 years, frequently concentrating on hospitals, healthcare technology and Catholic communications. He has also specialized in fundraising communications, ghostwriting for CEOs of local, national and global charities, nonprofits and foundations.

Around the web

Given the precarious excitement of the moment—or is it exciting precarity?—policymakers and healthcare leaders must set directives guiding not only what to do with AI but also when to do it. 

The final list also included diabetes drugs sold by Boehringer Ingelheim and Merck. The first round of drug price negotiations reduced the Medicare prices for 10 popular drugs by up to 79%. 

HHS has thought through the ways AI can and should become an integral part of healthcare, human services and public health. Last Friday—possibly just days ahead of seating a new secretary—the agency released a detailed plan for getting there from here.