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Can AI and radiographs help in resource-poor areas for the fight against COVID-19?

Pooja Rao
Tarun Raj
Manoj TLD
Preetham Srinivas
Bhagarva Reddy

By POOJA RAO, TARUN RAJ, BHARGAVA REDDY, MANOJ TLD, and PREETHAM SRINIVAS

In March 2020, we re-purposed our chest X-ray AI tool, qXR, to detect signs of COVID-19. We validated it on a test set of 11479 CXRs with 515 PCR-confirmed COVID-19 positives. The algorithm performs at an AUC of 0.9 (95% CI : 0.88 – 0.92) on this test set. At our most common operating threshold for this version, sensitivity is 0.912 (95% CI : 0.88 – 0.93) and specificity is 0.775 (95% CI : 0.77 – 0.78). qXR for COVID-19 is used at over 28 sites across the world to triage suspected patients with COVID-19 and to monitor the progress of infection in patients admitted to hospital

The emergence of the COVID-19 pandemic has already caused a great deal of disruption around the world. Healthcare systems are overwhelmed as we speak, in the face of WHO guidance to ‘test, test, test’ [1]. Many countries are facing a severe shortage of Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests. There has been a lot of debate around the role of radiology — both chest X-rays (CXRs) and chest CT scans — as an alternative or supplement to RT-PCR in triage and diagnosis. Opinions on the subject range from ‘Radiology is fundamental in this process’ [2] to ‘framing CT as pivotal for COVID-19 diagnosis is a distraction during a pandemic, and possibly dangerous’ [3].

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