Researchers Designed First of Its Kind AI Tool Detecting COVID-19 Probability

A study in the Journal of Medical Internet Research has introduced Biocogniv’s new AI-COVID software that efficiently predicts the probability of COVID-19 infection.

The researcher’s team at the University of Vermont and Cedars-Sinai has devised high accuracy predicting the COVID-19 infection probability using routine blood tests. This helps hospitals reduce the number of patients referred for scarce PCR (Polymerase chain reaction) testing.

Timothy Plante, M.D., M.H.S, Lead Author and University of Vermont Assistant Professor said, “Nine months into this pandemic, we now have a better understanding of how to care for patients with COVID-19, but there’s still a big bottleneck in COVID-19 diagnosis with PCR testing.”

The current standard diagnostic for COVID-19, PCR testing, needs specific sampling, such as a nasal swab, and a piece of specialized laboratory equipment to run.

“According to data from over 100 US hospitals, the national average turnaround time for COVID-19 tests ordered in emergency rooms is above 24 hours, far from the targeted one-hour turnaround,” Biocogniv Chief Operating Officer Tanya Kanigan, Ph.D., said.

Standard laboratory tests ordered by emergency departments include Complete Blood Count and Complete Metabolic Panels having a rapid turnaround time. These tests give an insight into the immune system, kidney, electrolytes, and liver. The researchers successfully trained a model that could analyze changes in these routine tests and assign a probability of the patient being COVID-19 negative with accuracy.

Jennifer Joe, M.D., an emergency physician in Boston, Mass. and Biocogniv’s Chief Medical Officer, said, “AI-COVID takes seconds to generate its informative result once these blood tests return, which can then be incorporated by the laboratory into its own test interpretation.”

Cedars-Sinai pulmonary and internal medicine specialist Victor Tapson, M.D., says such assistive tools that prove helpful for physicians to rule out possible diagnoses are familiar in emergency medicine. “For example, a low D-dimer blood test can help us rule out clots in certain patients, allowing providers to skip expensive, often time-consuming diagnostics such as chest CT scans,” said Tapson.

As per the Biocogniv team, a secondary benefit of laboratories incorporating AI-COVID can reduce the time for traditional PCR results. “With the help of AI-COVID, laboratories might relieve some of the testing bottlenecks by helping providers better allocate rapid PCR testing for patients who really need it,” said Joe. The AI-COVID model was built on real-world data from Cedars-Sinai as well as on data from demographically and geographically diverse patients that was gathered from 22 US hospitals, capturing an area under the curve (or AUC) of 0.91 out of 1.00.

Biocogniv Chief Scientific Officer George Hauser, MD, a pathologist, said, “This enables the model to achieve a high sensitivity of 95 percent while maintaining moderate specificity of 49 percent, which is very similar to the performance of other commonly used rule-out tests.”

“I’m honored to have such an impressive team of medical scientists from the University of Vermont and Cedars-Sinai as collaborators invalidating this timely model,” Biocogniv CEO Artur Adib, Ph.D., said. “AI has progressed considerably; the time is now to leverage this powerful tool for new healthcare breakthroughs, and we’re glad to direct it to help hospital laboratories and providers combat the current COVID-19 crisis,” added Adib.