Award Abstract #2026809

RAPID: Explainable Machine Learning for Analysis of COVID-19 Chest CT

See grant description on NSF site

Program Manager:

Sylvia Spengler

Active Dates:

Awarded Amount:



Michael J Pazzani

Albert Hsiao

Awardee Organization:

University of California-San Diego


Computer and Information Science and Engineering (CISE)


In December 2019, it was discovered that a widely contagious pneumonia was caused by a new coronavirus infection now named COVID-19. The primary test for detection of the virus is real-time polymerase chain reaction (RT-PCR) with sensitivity of approximately 71% in some studies. However, this test may require several days to provide a result. Perhaps more importantly, imaging with x-ray or computed tomography (CT) are required to confirm pneumonia, which is the principal cause of death, as it leads to acute respiratory distress syndrome (ARDS). Recent studies have shown sensitivity of chest CT for approximately 98% for COVID-19 pneumonia and could provide immediate results but currently require human interpretation. Given the need for rapid, more accurate diagnosis, this project will use, adapt, and evaluate explainable machine learning techniques to diagnosis of COVID-19 pneumonia. This project will improve the understanding of mechanisms of COVID-19 and will help mitigate its impacts.Viral nucleic acid detection using real-time polymerase chain reaction (RT-PCR) is the primary method for diagnosis of COVID-19 infection, which has rapidly spread worldwide as a global pandemic. Sensitivity of this test for COVID-19 infection has been estimated at approximately 71% in some studies and may require several days for a result. X-ray and CT imaging are complementary technologies that allow diagnosis of COVID-19 pneumonia, which can evolve to acute respiratory distress syndrome (ARDS) -- the principal cause of death in patients with COVID-19 infection. Especially early in the course of the disease, chest CT has multiple advantages over RT-PCR yielding results more quickly and is already widely deployed, but requires expert radiologist interpretation. The number of chest CTs may rapidly exceed the speed and capacity of already strained radiologists. An explainable machine learning algorithm may address this disadvantage to expedite the interpretation of chest CT and assist rapid triage of patients to the ICU, inpatient ward, monitoring unit, or home self-quarantine. Machine learning algorithms, specifically those leveraging deep convolutional neural networks (deep learning), have the potential for facilitating even more rapid diagnosis within minutes. This project seeks to validate the use of explainable deep learning methods to adjust diagnostic operating points for multiple applications, including (a) disease screening, (b) disease staging and prognostication, and (c) evaluation of treatment response.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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