A new artificial intelligence (AI) platform developed by Northwestern University researchers can detect COVID-19 in the lungs 10 times faster and a bit more accurately than specialized cardiothoracic radiologists, according to a study published today in Radiology. The researchers trained and tested DeepCOVID-XR, a machine-learning algorithm that analyzes chest X-rays, on 17,002 X-ray images, 5,445 of them with signs of COVID-19, collected from February to April. When pitted against five experienced cardiothoracic radiology subspecialists, DeepCOVID-XR analyzed each of 300 randomly selected test images in about 18 minutes, versus the 2.5 to 3.5 hours of individual radiologists. DeepCOVID-XR was 82% accurate, compared with the radiologists' 76% to 81% individually and 81% as a team. "These are experts who are sub-specialty trained in reading chest imaging, whereas the majority of chest X-rays are read by general radiologists or initially interpreted by non-radiologists, such as the treating clinician," lead author Ramsey Wehbe, MD, said in a Northwestern news release.

Algorithm outperformed thoracic radiologists in detecting COVID-19 in new study. Northwestern University researchers have developed a new artificial intelligence (A.I.) platform that detects COVID-19 by analyzing X-ray images of the lungs. Called DeepCOVID-XR, the machine-learning algorithm outperformed a team of specialized thoracic radiologists -- spotting COVID-19 in X-rays about 10 times faster and 1-6% more accurately. The researchers believe physicians could use the A.I. system to rapidly screen patients who are admitted into hospitals for reasons other than COVID-19. Faster, earlier detection of the highly contagious virus could potentially protect health care workers and other patients by triggering the positive patient to isolate sooner.

IMAGE: Generated heatmaps appropriately highlighted abnormalities in the lung fields in those images accurately labeled as COVID-19 positive (A-C) in contrast to images which were accurately labeled as negative for COVID-19... view more Called DeepCOVID-XR, the machine-learning algorithm outperformed a team of specialized thoracic radiologists -- spotting COVID-19 in X-rays about 10 times faster and 1-6% more accurately. The researchers believe physicians could use the A.I. system to rapidly screen patients who are admitted into hospitals for reasons other than COVID-19. Faster, earlier detection of the highly contagious virus could potentially protect health care workers and other patients by triggering the positive patient to isolate sooner. The study's authors also believe the algorithm could potentially flag patients for isolation and testing who are not otherwise under investigation for COVID-19. The study will be published on Nov. 24 in the journal Radiology.

According to Business Insider, 10 million self-driving cars are expected to hit the road by 2020. For many, the prospect of taking trips with unmanned vehicles may seem akin to magic, but the capability is actually the result of machine learning, a form of artificial intelligence that uses algorithms designed to learn from and respond according to the data it receives. In the transportation industry, machine learning is the driving force behind many burgeoning technological advances. On Wednesday, October 26, industry and academic experts gathered in Northwestern's McCormick Auditorium for "Machine Learning in Transportation," a technical workshop hosted by the Northwestern University Transportation Center and Northwestern's Center for the Commercialization of Innovative Transportation Technology that featured speakers from Northwestern, BMW, IBM, and more. "Machine learning allows us to tackle tasks that are too difficult to solve with fixed programs written and designed by human beings," said Aggelos Katsaggelos, Joseph Cummings Professor of Electrical Engineering and Computer Science at the McCormick School of Engineering.