The Mount Sinai team integrated data from those CT scans with the clinical information to develop an AI algorithm. It mimics the workflow a physician uses to diagnose COVID-19 and gives a final prediction of positive or negative diagnosis. The AI model produces separate probabilities of being COVID-19-positive based on CT images, clinical data, and both combined. Researchers initially trained and fine-tuned the algorithm on data from 626 out of 905 patients, and then tested the algorithm on the remaining 279 patients in the study group (split between COVID-19-positive and negative cases) to judge the test's sensitivity; higher sensitivity means better detection performance. The algorithm was shown to have statistically significantly higher sensitivity (84 percent) compared to 75 percent for radiologists evaluating the images and clinical data. The AI system also improved the detection of COVID-19-positive patients who had negative CT scans. Specifically, it recognized 68 percent of COVID-19-positive cases, whereas radiologists interpreted all of these cases as negative due to the negative CT appearance. Improved detection is particularly important to keep patients isolated if scans don't show lung disease when patients first present symptoms (since the previous study showed that lung disease doesn't always show up on CT in the first few days) and COVID-19 symptoms are often nonspecific, resembling a flu or common cold, so it can be difficult to diagnose.

CT scans are not widely used for diagnosis of COVID-19 in the United States; however, Dr. Fayad explains that imaging can still play an important role.


"Imaging can help give a rapid and accurate diagnosis--lab tests can take up to two days, and there is the possibility of false negatives--meaning imaging can help isolate patients immediately if needed, and manage hospital resources effectively. The high sensitivity of our AI model can provide a 'second opinion' to physicians in cases where CT is either negative (in the early course of infection) or shows nonspecific findings, which can be common. It's something that should be considered on a wider scale, especially in the United States, where currently we have more spare capacity for CT scanning than in labs for genetic tests," said Dr. Fayad, who is also a Professor of Diagnostic, Molecular and Interventional Radiology at the Icahn School of Medicine at Mount Sinai.

"This study is important because it shows that an artificial intelligence algorithm can be trained to help with early identification of COVID-19, and this can be used in the clinical setting to triage or prioritize the evaluation of sick patients early in their admission to the emergency room," says Matthew Levin, MD, Director of the Mount Sinai Health System's Clinical Data Science Team, and a member of the Mount Sinai COVID Informatics Center. "This is an early proof concept that we can apply to our own patient data to further develop algorithms that are more specific to our region and diverse populations."

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