HeartLung.AI lands 10 ESC 2026 abstract acceptances

By AI, Created 4:55 PM UTC, May 18, 2026, /AGP/ – HeartLung.AI says 10 scientific abstracts were accepted for ESC Congress 2026 in May, giving the Houston company an unusually large presence at the European Society of Cardiology’s annual meeting. The studies highlight AI-driven CT screening for coronary calcium, chamber volumetry, lung cancer risk, plaque characterization and other preventive cardiology uses.

Why it matters: - HeartLung.AI is pushing a broader use of routine CT scans to find hidden cardiovascular and related disease risk earlier. - The accepted ESC.26 work spans coronary artery calcium, cardiac chamber volume, plaque, aortic stenosis, lung cancer risk and vascular risk. - The company’s FDA-cleared AI-CVD platform is moving from single-finding image analysis toward multi-biomarker prevention screening. - HeartLung.AI says it will be the only company at ESC Congress 2026 presenting 10 accepted scientific studies.

What happened: - HeartLung.AI announced on May 11, 2026, that 10 scientific abstracts were accepted for presentation at ESC Congress 2026 in Barcelona. - ESC Congress is the annual meeting of the European Society of Cardiology. - The abstracts cover AI-enabled cardiovascular imaging and preventive cardiology. - The company framed the acceptances as evidence of its growing research footprint in AI-CVD.

The details: - The accepted studies include automated coronary artery calcium assessment and next-generation calcium quantification. - The research also includes AI-derived cardiac chamber volumetry from routine chest CT scans. - Additional abstracts focus on cardiometabolic phenotyping, plaque characterization, vascular risk assessment and aortic stenosis prediction. - Several studies use non-gated chest CT or routine coronary artery calcium scans that were not acquired for dedicated cardiac analysis. - One abstract evaluates Sybil AI for lung cancer risk assessment from cardiac CT scans versus lung CT scans. - Another looks at long-term lung cancer risk prediction using Sybil AI on routine CAC scans in MESA over 15 years. - A separate study compares AI-based cardiac chamber volumetry from non-gated chest CT with echocardiography for predicting heart failure and atrial fibrillation. - Another MESA study uses chamber volumetry from non-gated chest CT to predict heart failure and atrial fibrillation. - Agatston 2.0 aims to detect subtle coronary calcification in people with a CAC score of zero. - That work is designed to refine the meaning of the “Power of Zero” by identifying higher-risk patients who may be missed by traditional scoring. - A Miami Heart Study abstract tests whether cardiometabolic phenotypes from CAC scans can predict obstructive and high-risk plaque on coronary CT angiography. - Another Miami Heart Study abstract links epicardial adipose tissue analysis with increased non-calcified plaque burden in non-obese people with low CAC scores. - Another study evaluates AI-derived calcium burden and plaque density profiling for risk stratification among CAC scores of 1-99. - A deep learning model is used to predict aortic stenosis from routine CAC scans in MESA. - Another abstract tests whether AI-derived left atrial volume index and left atrial/right atrial and left atrial/left ventricular ratios from CAC scans predict long-term atrial fibrillation and stroke. - The company says the platform is designed to extract multiple clinically meaningful biomarkers from existing imaging.

Between the lines: - The accepted abstracts suggest HeartLung.AI is trying to turn a common test into a broader screening tool for multiple diseases. - The repeated use of MESA and Miami Heart Study data points to an effort to validate the platform across established cohorts. - The comments from outside experts signal that the work is being positioned as a prevention strategy, not just an imaging upgrade. - Arthur Agatston said the next frontier is making CAC and CT-based prevention more informative, reproducible and actionable rather than replacing CAC. - Nathan D. Wong said AI-derived CT biomarkers could expand preventive cardiology by pulling coronary, cardiac, cardiometabolic, vascular and multisystem information from routine scans. - Robert A. Kloner said the accepted studies move CT imaging beyond isolated measurements toward an outcomes-oriented framework. - David F. Yankelevitz said the collaboration with Mount Sinai investigators focuses on extracting more clinically meaningful information from CT scans already being acquired.

What’s next: - The 10 abstracts will be presented at ESC Congress 2026. - HeartLung.AI says the work is part of a broader effort to support earlier intervention, improve patient engagement and identify high-risk individuals before advanced disease develops. - The company lists continued collaboration with academic physicians and researchers as central to building out the AI-CVD platform. - HeartLung Technologies says its preventive imaging platform also targets lung cancer, COPD, osteoporosis and fatty liver disease on CT scans.

The bottom line: - HeartLung.AI is using a major ESC 2026 showing to argue that routine CT scans can become a multi-disease prevention tool, not just a source of single-number cardiac risk scores.