  We predict mechanical ventilation requirement and mortality using
computational modeling of chest radiographs (CXRs) for coronavirus disease 2019
(COVID-19) patients. This two-center, retrospective study analyzed 530
deidentified CXRs from 515 COVID-19 patients treated at Stony Brook University
Hospital and Newark Beth Israel Medical Center between March and August 2020.
DL and machine learning classifiers to predict mechanical ventilation
requirement and mortality were trained and evaluated using patient CXRs. A
novel radiomic embedding framework was also explored for outcome prediction.
All results are compared against radiologist grading of CXRs (zone-wise expert
severity scores). Radiomic and DL classification models had mAUCs of
0.78+/-0.02 and 0.81+/-0.04, compared with expert scores mAUCs of 0.75+/-0.02
and 0.79+/-0.05 for mechanical ventilation requirement and mortality
prediction, respectively. Combined classifiers using both radiomics and expert
severity scores resulted in mAUCs of 0.79+/-0.04 and 0.83+/-0.04 for each
prediction task, demonstrating improvement over either artificial intelligence
or radiologist interpretation alone. Our results also suggest instances where
inclusion of radiomic features in DL improves model predictions, something that
might be explored in other pathologies. The models proposed in this study and
the prognostic information they provide might aid physician decision making and
resource allocation during the COVID-19 pandemic.
