Algorithm helps improve coronary calcium detection

Algorithm helps improve coronary calcium detection

Press releases may be edited for formatting or style | February 14, 2020 Artificial Intelligence Cardiology CT X-Ray
Dutch computer scientists and colleagues in the United States have achieved a marked improvement in the automatic detection of calcified atherosclerotic plaque in coronary arteries and thoracic aorta using computerized tomography (CT).

Reporting this Feb. 11 in the journal Radiology, they demonstrated that a deep-learning algorithm for artificial intelligence-assisted calcium scoring they developed can accurately determine cardiovascular risk across a range of CT scans and in a racially diverse population.

Deep-learning algorithms are a form of artificial intelligence that enable computers to “learn” from examples to perform a task. This one was developed and evaluated with the help of co-author J. Jeffrey Carr, MD, MSCE, the Cornelius Vanderbilt Chair in Radiology & Radiological Sciences in the Vanderbilt University School of Medicine.

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“Coronary calcium has been previously established as an excellent test for reclassifying an individual’s risk for heart disease as either high or low risk,” Carr said. “Developing a fully automated method that can perform the measurement of coronary calcium from CT scans accurately has a lot of value.

“I’m enthusiastic that versions of this could be implemented in (clinical) practice in a relatively few years and thus lower the barriers to identifying those people at high risk for heart disease,” he said.

The algorithm was trained and evaluated by the paper’s senior author, Ivana Išgum, PhD, a world leader in AI and medical imaging, her graduate student and first author, Sanne GM van Velzen, and colleagues at Amsterdam University Medical Center and University Medical Center Utrecht.

The work is based on a calcium scoring algorithm in the National Lung Screening Trial (NLST) that Išgum and graduate student Nikolas Lessmann developed in a collaboration between University Medical Center Utrecht and Radboud University Medical Center in Nijmegen.

The algorithm was built and evaluated using 7,240 CT scans, including nearly 2,900 from the Jackson Heart Study of African Americans in Jackson, Mississippi, 1,400 from patients treated for breast cancer in the Netherlands, and more than 1,000 from the NLST, which was conducted in 2002-2004.

Carr helped plan the study and gained access to the CT scans from the Jackson Heart Study, which is supported by the National Heart, Lung, and Blood Institute (NHLBI) of the National Institutes of Health (NIH), and from the NLST, which was supported by the National Cancer Institute.

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