AI is Revolutionizing Cardiovascular Risk Assessments
In a new study conducted by Cedars-Sinai, researchers have leveraged artificial intelligence to significantly advance how cardiovascular risks are assessed, making the process both less expensive and less invasive.
Published in Nature Communications, the study reveals that routine chest computed tomography (CT) scans, typically performed without contrast, can now accurately evaluate coronary calcium levels and the dimensions of heart chambers and muscles thanks to AI technology.
Piotr J. Slomka, Ph.D., Director of Innovation in Imaging at Cedars-Sinai and a professor of Medicine in the Division of Artificial Intelligence in Medicine, emphasizes the potential of these findings to transform clinical practices.
“These results are likely practice-changing for many patients because this technology can accurately identify cardiovascular risk without the need for invasive tests or contrast dye, which some patients cannot tolerate,” Slomka stated.
Currently, more than 15 million CT scans are conducted annually in the U.S., with a substantial number not fully utilized for detailed cardiac evaluations. Traditionally, assessing cardiovascular risks through CT scans involves the use of contrast agents, which can pose risks to patients with certain health conditions.
Slomka’s team has developed a novel AI algorithm that extracts critical heart health insights from standard, less costly scans that use minimal radiation. This innovation holds the promise of integrating detailed heart evaluations into regular diagnostic routines.
The AI-driven approach developed by Cedars-Sinai researchers utilizes two AI models to analyze data on coronary calcium and heart muscle chamber sizes from nearly 30,000 patient imaging records. This method proved to be a more reliable indicator of cardiac risk than traditional evaluations performed by radiologists.
Sumeet Chugh, MD, Director of the Division of Artificial Intelligence in Medicine at Cedars-Sinai, who was not directly involved in the study, highlighted the broader implications of this technology.
“This technology allows for the large-scale use of existing CT data to identify individuals at risk sooner,” Chugh explained. “Coronary artery disease remains the leading cause of disability and death globally. These findings demonstrate how AI tools can utilize CT images initially performed for lung disease investigations, offering a cost-effective, significant public health impact on heart disease.”
This study not only showcases the potential of AI in enhancing the efficiency and safety of medical diagnostics but also paves the way for its broader application in preventive medicine. By enabling the early detection of cardiovascular risks through readily available CT scans, AI can help mitigate the prevalence of heart disease, which continues to be a major global health challenge.
Originally posted on OpenDataScience.com
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