Researchers at Mount Sinai Health System used artificial intelligence (AI) to rapidly detect COVID-19 based on how lung disease looks in computed tomography (CT scans) of the chest in combination with clinical data, the health system announced in a press release.
The results of the research were published in the May 19 issue of Nature Medicine.
“We were able to show that the AI model was as accurate as an experienced radiologist in diagnosing the disease, and even better in some cases where there was no clear sign of lung disease on CT,” says one of the lead authors, Zahi Fayad, PhD, Director of the BioMedical Engineering and Imaging Institute (BMEII) at the Icahn School of Medicine at Mount Sinai.
This research expands on a previous Mount Sinai study that identified a characteristic pattern of disease in the lungs of COVID-19 patients and showed how it develops over the course of a week and a half.
The new study involved scans of more than 900 patients that Mount Sinai received from institutional collaborators at hospitals in China. The patients were admitted to 18 medical centers in 13 Chinese provinces between January 17 and March 3, 2020. The scans included 419 confirmed COVID-19-positive cases (most either had recently traveled to Wuhan, China, where the outbreak began, or had contact with an infected COVID-19 patient) and 486 COVID-19-negative scans.
Researchers also had patients’ clinical information, including blood test results showing any abnormalities in white blood cell counts or lymphocyte counts as well as their age, sex, and symptoms (fever, cough, or cough with mucus). They focused on CT scans and blood tests since doctors in China use both of these to diagnose patients with COVID-19 if they come in with fever or have been in contact with an infected patient.
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.
