The study of the new detection tool was published in Nature Medicine, where researchers used AI algorithms and chest CT scans to find out whether patients were positive for COVID-19.
Icahn School of Medicine at Mount Sinai director of the BioMedical Engineering and Imaging Institute, Dr. Zahi Fayad, said the technology helped improve detection in cases where patients presented normal CT scans, and the study used data from the CT scans from more than 900 patients with clinical information to develop the AI algorithm.
“[Faculty members working with an international team implemented] a novel AI model using CT data from coronavirus patients in Chinese medical centres [and] 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,” Dr. Fayad said.
“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 State, where currently we have more spare capacity for CT scanning than in labs for genetic tests,” he said.
Mount Sinai Health System director of the clinical data science team, Dr. Matthew Levin, said the next steps will be to further develop the model to help forecast patient outcomes, believing the tool can easily be deployed worldwide to other hospitals, either online or integrated into their own systems.
“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 prioritise the evaluation of sick patients early in their admission to the emergency room,” Dr. Levin said.