AI Predicts COVID Prognosis at Near-Expert Level Based Off CT Scans

Investigators at the Medical University of South Carolina (Charleston, SC, USA) evaluated the ability of a deep convolutional neural network (dCNN) to predict inpatient outcomes associated with COVID-19 pneumonia. For the study, a previously trained dCNN was tested on an external validation cohort of 241 patients who presented to the emergency department and received a chest CT scan, 93 with COVID-19 and 168 without. Airspace opacity scoring systems were defined by the extent of airspace opacity in each lobe, totaled across the entire lungs. Expert and dCNN scores were concurrently evaluated for interobserver agreement, while both dCNN identified airspace opacity scoring and raw opacity values were used in the prediction of COVID-19 diagnosis and inpatient outcomes.

The study revealed that interobserver agreement for airspace opacity scoring was 0.892 (95% CI 0.834-0.930). The probability of each outcome behaved as a logistic function of the opacity scoring (25% ICU admission at score of 13/25, 25% intubation at 17/25, and 25% mortality at 20/25). The study also found that the length of hospitalization, ICU stay, and intubation were associated with larger airspace opacity score (p = 0.032, 0.039, 0.036, respectively). Based on these findings, the researchers concluded that the tested dCNN was highly predictive of inpatient outcomes, can perform at a near expert level, and provide added value for clinicians in terms of prognostication and disease severity.

“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,” explained corresponding author U. Joseph Schoepf, MD, from the Division of Cardiovascular Imaging at the Medical University of South Carolina in Charleston. “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.”

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