New research details how a deep convolutional neural network (dCNN) uses a scoring system to predict prognosis for COVID inpatients at a nearly expert level based off CT scans. 

The dCNN forecasted hospital stay, intensive care unit admission and intubation with an interobserver agreement of 0.892 when scoring chest CT images from hospitalized patients with COVID pneumonia. Experts involved in the study suggest that the dCNN could assist clinicians who treat COVID patients with assessing disease severity and other prognostic factors that might direct treatment plans. 

“The use of artificial intelligence deep learning models to prognosticate from CT images has been identified from the beginning of the pandemic as a potential way to expedite the triage process, improve prognostication, and guideline utilization of resources” corresponding author U. Joseph Schoepf, MD, from the Division of Cardiovascular Imaging at the Medical University of South Carolina in Charleston, and co-authors explained. “Utilizing AI severity scoring may be helpful in meeting the challenge of practical, reproducible triage of COVID-19 patients by identifying patients at high risk for morbidity and mortality.” 

The authors noted that severity scoring systems help to strengthen clinical decision-making processes by providing high quality, evidence-based protocols using image features from specific pathologies. However, manual segment severity scoring is both challenging and time consuming. Using artificial intelligence to streamline similar diagnostic processes during patient triage has proved beneficial since the beginning of the pandemic, but few studies have specifically analyzed AI’s efficacy for scoring disease severity based on chest CT images of COVID patients. And many that have examined its effectiveness have been based on public datasets, not real-world cohorts.