HeartLung Technologies has announced that its AI-enabled Automated Cardiac Chambers Volumetry (AutoChamber) has received Breakthrough Device designation and marketing authorization from the U.S. Food and Drug Administration (FDA). This AI tool is designed for early detection of heart disease and other conditions through analysis of cardiac chamber volumes.
How AutoChamber AI Works
AutoChamber AI reports the volume of cardiac chambers and left ventricular wall mass using non-contrast computed tomography (CT) scans, including heart scans for coronary artery calcium (CAC) score and lung scans for lung cancer screening. It also functions with coronary CT angiography (CCTA) scans. The AI takes approximately 15-20 seconds to process and flag patients, generating a quantitative imaging report with percentiles and cutoff points to guide physician action.
Clinical Significance
According to Dr. Morteza Naghavi, founder and president of HeartLung Technologies, AutoChamber AI can evaluate the risk of heart failure, atrial fibrillation, and stroke by adding its analysis to thoracic CT scans. Dr. David Yankelevitz, professor of radiology at Icahn School of Medicine at Mount Sinai Hospital, noted the potential for adding this AI to lung cancer screening programs, as these patients are at high risk for cardiovascular events. The AI creates a quantitative imaging report with appropriate percentiles and cutoff points that help physicians to take necessary actions.
Supporting Data and Studies
HeartLung Technologies presented several studies to the FDA comparing AI-enabled CT measurements with cardiac MRI, contrast-enhanced cardiac CT, NT-proBNP, CHARGE-AF Risk Score, ASCVD Pooled Cohort Equation, and PREVENT Risk Scores. These studies indicated that AutoChamber AI outperformed traditional epidemiological risk scores and predicted heart failure, atrial fibrillation, and stroke comparably to cardiac MRI and contrast-enhanced CT scans.
Impact on Healthcare
The "Breakthrough" value of AutoChamber AI lies in its ability to screen millions of CT scans done for other reasons, helping doctors detect high-risk patients for cardiovascular disease (CVD) and sudden cardiac death (SCD). Dr. Nathan Wong, a professor of medicine and epidemiology at University of California, Irvine, emphasized the potential for identifying individuals at increased risk who could benefit from earlier intervention. Dr. Anthony Reeves, professor of electrical engineering and computer vision at Cornell University, noted that AI-enabled algorithms like AutoChamber can fill the gap in radiologists' ability to efficiently visualize and quantitatively evaluate relevant diagnostic image features without imposing extra work.
Future Directions
HeartLung Technologies is also developing AI-CAC and AI-CVD, which combine coronary artery plaque calcification and cardiac chambers volume analysis to advance preventive cardiology. The company's broader goal is to build autonomous AI for early detection of heart disease, lung cancer, osteoporosis, and fatty liver disease.
