  Purpose: The Medical Imaging and Data Resource Center (MIDRC) open data
commons was launched to accelerate the development of artificial intelligence
(AI) algorithms to help address the COVID-19 pandemic. The purpose of this
study was to quantify longitudinal representativeness of the demographic
characteristics of the primary imaging dataset compared to the United States
general population (US Census) and COVID-19 positive case counts from the
Centers for Disease Control and Prevention (CDC). Approach: The Jensen Shannon
distance (JSD) was used to longitudinally measure the similarity of the
distribution of (1) all unique patients in the MIDRC data to the 2020 US Census
and (2) all unique COVID-19 positive patients in the MIDRC data to the case
counts reported by the CDC. The distributions were evaluated in the demographic
categories of age at index, sex, race, ethnicity, and the intersection of race
and ethnicity. Results: Representativeness the MIDRC data by ethnicity and the
intersection of race and ethnicity was impacted by the percentage of CDC case
counts for which data in these categories is not reported. The distributions by
sex and race have retained their level of representativeness over time.
Conclusion: The representativeness of the open medical imaging datasets in the
curated public data commons at MIDRC has evolved over time as both the number
of contributing institutions and overall number of subjects has grown. The use
of metrics such as the JSD support measurement of representativeness, one step
needed for fair and generalizable AI algorithm development.
