New AI software a low-cost, efficient option for coronary artery calcium scoring
Using artificial intelligence-based software for coronary artery calcium scoring (CACS) is comparable to manual methods and can be universally applied across a range of vendors and CT scanners, according to research published Tuesday.
CAC scores play a vital role in diagnosing and treating coronary artery disease (CAD). Typically, they are completed via ECG-gated non-contrast-enhanced cardiac CT scans or non-gated chest CT. And while manual methods and radiologist expertise has shown to be as accurate as cardiac CT scans, the process is time consuming.
AI technology that can evaluate information from both chest CT and cardiac CT enables CAC scores to be completed more frequently and in less time.
In this study, the diagnostic performance of AI-driven CACS in a clinical setting was evaluated. The authors enrolled 901 patients who had undergone both chest CT and ECG-gated non-contrast-enhanced cardiac CT with the same equipment. AI-CACS software was based on a deep learning algorithm and trained on multi-vendor, multi-scanner and multi-hospital data from a chest CT database.
The results showed a 95% agreement between both methods, with a concordance rate of 80.6%. AI-developed risk categories were similar across three different types of machines. Overall, the AI-CACS and manual CACS showed good agreement in terms of risk category.
CAD is a leading cause of death worldwide, but the authors indicate that using AI-CACS could detect diseases before patients begin to develop symptoms.
“Determination of the AI-CACS by chest CT could serve as a low-cost and labor-effective strategy for identifying CAD risk categories,” Yu Zhang, with the Department of Radiology at the Second Affiliated Hospital of Chongqing Medical University in China, and co-authors wrote. “The AI-CACS software algorithm has good clinical universality,” they continued.
You can read the full study in the European Journal of Radiology.
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