  X-ray and computed tomography (CT) scanning technologies for COVID-19
screening have gained significant traction in AI research since the start of
the coronavirus pandemic. Despite these continuous advancements for COVID-19
screening, many concerns remain about model reliability when used in a clinical
setting. Much has been published, but with limited transparency in expected
model performance. We set out to address this limitation through a set of
experiments to quantify baseline performance metrics and variability for
COVID-19 detection in chest x-ray for 12 common deep learning architectures.
Specifically, we adopted an experimental paradigm controlling for
train-validation-test split and model architecture where the source of
prediction variability originates from model weight initialization, random data
augmentation transformations, and batch shuffling. Each model architecture was
trained 5 separate times on identical train-validation-test splits of a
publicly available x-ray image dataset provided by Cohen et al. (2020). Results
indicate that even within model architectures, model behavior varies in a
meaningful way between trained models. Best performing models achieve a false
negative rate of 3 out of 20 for detecting COVID-19 in a hold-out set. While
these results show promise in using AI for COVID-19 screening, they further
support the urgent need for diverse medical imaging datasets for model training
in a way that yields consistent prediction outcomes. It is our hope that these
modeling results accelerate work in building a more robust dataset and a viable
screening tool for COVID-19.
