Trained to see patterns by analyzing thousands of chest X-rays, a computer program predicted with up to 80 percent accuracy which patients with coronavirus disease (COVID-19) would develop life-threatening complications within 4 days, a new study finds. 

Developed by researchers at NYU Grossman School of Medicine, the program used several hundred gigabytes of data gleaned from 5,224 chest X-rays taken from 2,943 seriously ill patients infected with SARS-CoV-2, the virus behind the infections. 

The authors of the study, which was published in the journal npj Digital Medicine online May 12, cited the “pressing need” for the ability to quickly predict which patients with COVID-19 are likely to have lethal complications so that treatment resources can best be matched to those at increased risk. For reasons not yet fully understood, the health of some patients with the disease suddenly worsens, requires intensive care, and increases their chances of dying.

In a bid to address this need, the NYU Langone team fed not only X-ray information into their computer analysis, but also patients’ age, race, and gender, along with several vital signs and laboratory test results, including weight, body temperature, and blood immune cell levels. Also factored into their mathematical models, which can learn from examples, was the need for a mechanical ventilator and whether each patient survived (2,405) or died (538) from their infections.

Researchers then tested the predictive value of the software tool on 770 chest X-rays from 718 other patients admitted for COVID-19 through the emergency department at NYU Langone hospitals from March 3 to June 28, 2020. The computer program accurately predicted four out of five infected patients who required intensive care and mechanical ventilation and/or died within four days of admission. 

“Emergency room physicians and radiologists need effective tools like our program to quickly identify those patients with COVID-19 whose condition is most likely to deteriorate quickly so that healthcare providers can monitor them more closely and intervene earlier,” says study co-lead investigator Farah Shamout, PhD, an assistant professor in computer engineering at New York University’s campus in Abu Dhabi. 

“We believe that our COVID-19 classification test represents the largest application of artificial intelligence in radiology to address some of the most urgent needs of patients and caregivers during the pandemic,” says Yiqiu “Artie” Shen, MS, a doctoral student at the NYU Center for Data Science.

Study senior investigator Krzysztof J. Geras, PhD, an assistant professor in the Department of Radiology at NYU Langone, says a major advantage to machine intelligence programs such as theirs is that its accuracy can be tracked, updated, and improved with more data. He says the team plans to add more patient information as it becomes available. He also says the team is evaluating what additional clinical test results could be used to improve their test model. 

Dr. Geras says he hopes, as part of further research, to soon deploy NYU Langone’s COVID-19 classification test to emergency physicians and radiologists. In the interim, he is working with physicians to draft clinical guidelines for its use.

Funding support for the study was provided by National Institutes of Health grants P41 EB017183 and R01 LM013316, and National Science Foundation grants HDR-1922658 and HDR-1940097.

Besides Dr. Geras, Dr. Shamout, and Shen, other NYU and NYU Langone researchers involved in this study are co-lead investigators Nan Wu; Aakash Kaku; Jungkyu Park; and Taro Makino; and co-investigators Stanislaw Jastrzebski; Duo Wong; Ben Zhang; Siddhant Dogra; Men Cao; Narges Sharif Razavian, PhD; David Kudlowitz, MD; Lea Azour, MD; William H. Moore, MD; Yvonne W. Lui, MD; Yindalon Aphinyanaphongs, MD, PhD; and Carlos Fernandez-Granda. 

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