The COVID-19 pandemic is laying bare the need for accessible curated datasets that
researchers can use to build clinical-grade artificial intelligence (AI) models.
Researchers in China recently used deep learning models of clinical-grade AI trained on
radiographic imaging at an exponential scale to detect COVID-19 cases and optimize
allocation of limited resources. (Jin S, Wang B, Xu H, et al. Ai-assisted ct imaging
analysis for covid-19 screening: Building and deploying a medical ai system in four
weeks. medRxiv. 2020:2020.2003.2019.20039354. doi: 10.1101/2020.03.19.20039354). This
research platform is currently not possible in the United States because there are no
large accessible radiographic image sets of COVID-19 patients to leverage. Therefore, the
purpose of this project is to launch an interactive and HIPAA-compliant web
portal-CovidImaging.com-where patients can securely share their radiographic imaging
data. This portal will serve as an imaging repository for the purpose of training,
testing, and validating an AI model aimed at earlier and more accurate disease detection
in this global fight against COVID-19.

On January 30, 2020 the World Health Organization designated the COVID-19 outbreak that
originated in Wuhan, China as a global health emergency. Since then, the virus has
rapidly spread across the world as a pandemic, unfavorably affecting health care systems
at the expense of primary healthcare requirements.1Symptomatic cases of COVID-19 present
with clinical symptoms similar to viral pneumonia such as fever, shortness of breath,
chills, fatigue, cough, and dyspnea that can progress to acute respiratory distress
syndrome, requiring critical care and ventilation. 2 Bronchoalveolar lavage analysis and
electron microscopy identified the causative agent to be a novel, positive-sense RNA
virus in the Coronaviridae family, with spiked peplomers attached to its envelope.3 This
family of viruses has also been associated with severe acute respiratory syndrome (SARS)
and Middle East respiratory syndrome (MERS), which cause similar pneumonia-related
mortality.

Preliminary reviews have been conducted to investigate the overlap of reported imaging
features in SARS, MERS, and COVID-19 as it relates to onset of symptoms, progression of
disease, and follow up. Early evidence suggests significant overlap in imaging features
such as subpleural and peripheral areas of ground-glass opacity and consolidation, with
initial chest imaging indicating abnormality in at least 85% of COVID-19 patients. 4 In
the absence of vaccines and specific therapeutic drugs for the prevention and treatment
of COVID-19, detection of the disease plays a vital role in containment strategies that
isolate infected people from the healthy population. Even though RT-PCR sensitivity for
COVID-19 can be as low as 60-70%, it is currently the large-scale method of testing with
its high specificity.7 The low sensitivity of RT-PCR, along with limitations of sample
collection, time delay, transportation, and lab equipment, means that not enough COVID-19
positive people are being identified in time to prevent progressive infection of this
highly contagious virus. Given the respiratory involvement in COVID-19 infections, chest
radiography has played an important role in screening, diagnosing, and developing
treatment plans for patients with COVID-19-related pneumonia. Therefore, combining
imaging with clinical and laboratory findings could facilitate the early diagnosis of
COVID-19.5 Early detection would speed up treatment and allow for early patient
isolation. This is essential for the implementation of public health surveillance,
containment, and response for a highly communicable disease in which transmission can
occur prior to onset of symptoms. Improving the precision of radiographic interpretation
with AI models may improve detection rate and patient prognosis and thus help to reduce
COVID-19 spread.

As recently reported, chest CT demonstrates common radiographic features in almost all
COVID-19 patients, including ground-glass 4 opacities, multifocal patchy consolidation,
and/or interstitial changes with a peripheral distribution. 8,9 Studies have also been
conducted to compare the efficacy and diagnostic value of chest CT to RT-PCR tests in
COVID-19 cases. A case report of 1014 patients in China concluded that chest CT has a
high sensitivity for diagnosis of COVID-19, with 60% to 93% of cases showing initial
positive CT diagnosis prior to the initial positive RT-PCR results. 10 Another study with
51 patients having chest CT and RT-PCR assay within 3 days showed that the sensitivity of
CT for COVID-19 infection was 98%, compared to 71% RT-PCR sensitivity.11 These studies
further indicate the diagnostic value of chest radiographs alongside clinical and
laboratory findings. This project will develop a large radiographic image repository
which will be used to train and validate an AI deep learning model. This project
necessarily involves not only designing and refining a deep learning model, but also
curating a repository of donated chest radiographs that will be used to train the novel
model. Using a secure and HIPAA-complaint online platform, as has been designed for this
project, will allow this project to employ a big data approach to improve the accuracy of
the model. Since patients from healthcare facilities around the country will have equal
opportunity to participate in the project, this portal will also provide an opportunity
to expand the demographic pool in a way that previous studies could not.