Coronary Artery Calcium (CAC) scores have become increasingly important in assessing risk for coronary artery disease. Although non-contrast CT scans are typically used for CAC scoring, calculating CAC scores from CTAs directly could add further value to CTAs as a first-line diagnostic.

A study conducted by researchers from Nanjing University, Keya Medical, and the Medical University of South Carolina, has demonstrated the accuracy of a deep-learning method to quantify CAC scores from a single CTA scan, with 93% of scans categorized correctly.

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About Keya Medical

Keya Medical is an international medical technology company developing deep learning-based medical devices for disease diagnosis and treatment. The company is committed to creating solutions that deliver clinical value at all stages in the patient care process, covering specialties including cardiology, neurology, pulmonology, pathology, and surgery. Since 2016, Keya Medical has collaborated with more than 725 hospitals to improve diagnostics, care processes, and clinical outcomes. To learn more about Keya Medical, follow us on LinkedIn, Twitter, and Facebook.