HeartLung.AI Emerges as a Leading Innovator in Cardiology with 10 Accepted ESC.26 Abstracts

Accepted studies showcase HeartLung.AI’s leadership in AI-CVD, opportunistic CT screening, Agatston 2.0, chamber volumetry, and preventive cardiology.

HOUSTON, TX, UNITED STATES, May 11, 2026 /EINPresswire.com/ -- HeartLung.AI today announced that 10 scientific abstracts have been accepted for presentation at ESC Congress 2026, the annual meeting of the European Society of Cardiology. The accepted studies showcase HeartLung.AI’s growing leadership in AI–enabled cardiovascular imaging and preventive cardiology, spanning automated coronary artery calcium assessment, AI–derived cardiac chamber volumetry, cardiometabolic phenotyping, lung cancer risk prediction from cardiac CT scans, plaque characterization, aortic stenosis prediction, and vascular risk assessment.

The breadth of accepted work reflects the evolution of HeartLung.AI’s FDA–cleared AI–CVD platform from single–finding image analysis to a comprehensive prevention engine capable of extracting multiple clinically meaningful biomarkers from routine CT scans, including scans obtained for noncardiac indications.

HeartLung.AI will be the only company at ESC Congress 2026 presenting this high number of accepted scientific studies. The achievement reflects the strength of the company’s research program and the dedication of the clinicians, scientists, engineers, and collaborators whose work continues to advance AI–based preventive cardiology.

“We are extremely proud of the innovative and hardworking researchers who make HeartLung.AI what it is,” said Morteza Naghavi, MD, Founder of HeartLung.AI. “These 10 accepted abstracts demonstrate the scientific depth behind our mission to turn routine CT imaging into a powerful tool for earlier detection, better risk stratification, and broader cardiovascular prevention. I want to extend a very special thank you to the leading academic physician researchers who have participated in HeartLung.AI’s AI–CVD research and helped establish the scientific foundation for this platform. Their guidance, rigor, and collaboration have been essential to advancing our vision of image–guided prevention.”

“As an early developer of the coronary calcium score, I view the next frontier not as replacing CAC, but as making CAC and CT–based prevention more informative, reproducible, and actionable,” said Arthur Agatston, MD, FACC, preventive cardiologist, founder of The Agatston Center, and developer of the Agatston calcium score. “HeartLung.AI’s work on Agatston 2.0 and AI–enabled analysis of routine CT scans represents an important step toward detecting hidden cardiovascular risk earlier, including among patients whose traditional calcium score may underestimate their true risk.”

“These ESC.26 abstracts illustrate how AI–derived CT biomarkers can improve long–term cardiovascular risk stratification beyond traditional risk factors alone,” said Nathan D. Wong, PhD, MPH, FACC, FAHA, FNLA, MASPC, Professor and Director of the Heart Disease Prevention Program, Mary and Steve Wen Cardiovascular Division, and Co–Director of the Center for Global Cardiometabolic Health and Nutrition at the University of California, Irvine. “The ability to extract coronary, cardiac chamber, cardiometabolic, vascular, and multisystem information from routine CT imaging could substantially expand the reach of preventive cardiology.”

“The scientific program behind AI–CVD is notable because it connects image findings with clinically meaningful outcomes,” said Robert A. Kloner, MD, PhD, Chief Science Officer and Director of Cardiovascular Research at Huntington Medical Research Institutes. “The accepted ESC.26 studies demonstrate that AI can move CT imaging beyond isolated measurements and toward a broader, outcomes–oriented framework for identifying patients who may benefit from earlier preventive intervention.”

“The collaboration between HeartLung.AI and Mount Sinai investigators has focused on extracting more clinically meaningful information from CT scans that are already being acquired,” said David F. Yankelevitz, MD, Professor of Radiology and Director of the Lung Biopsy Service at the Icahn School of Medicine at Mount Sinai. “The lung cancer risk prediction abstracts are especially important because they point to a future in which cardiac and chest CT scans can contribute to more personalized, risk–based detection strategies across cardiology and lung cancer prevention.”

Accepted ESC.26 Abstracts

1. Performance of Sybil AI for lung cancer risk assessment: a head–to–head comparison of cardiac versus lung CT scans
This study evaluates whether Sybil AI, originally developed for lung CT scans, can also assess lung cancer risk from routine cardiac CT scans used for coronary artery calcium scoring. The findings support the potential for opportunistic lung cancer risk assessment from cardiac CT imaging.
2. AI–enabled cardiac chamber volumetry from non–gated chest CT versus echocardiography for prediction of heart failure and atrial fibrillation
This AI–CVD study compares AI–derived cardiac chamber measurements from non–gated chest CT against echocardiography for predicting heart failure and atrial fibrillation. The work supports the use of routine chest CT as a scalable tool for identifying structural heart disease risk without additional imaging.
3. Long–term lung cancer risk prediction using Sybil AI on routine coronary artery calcium scans: a 15–year study in MESA
This study examines whether routine coronary artery calcium scans can be used for long–term lung cancer risk prediction. The research supports the broader concept that CT scans obtained for cardiovascular screening may contain additional clinically meaningful information beyond coronary calcium alone.
4. Heart failure and atrial fibrillation prediction from non–gated chest CT scans using AI–based cardiac chamber volumetry within MESA
This study uses AI–based cardiac chamber volumetry from non–gated chest CT scans to predict heart failure and atrial fibrillation. The findings highlight the ability of AI–CVD to extract cardiac risk markers from existing CT scans that were not originally acquired for dedicated cardiac chamber analysis.
5. Agatston 2.0: a next–generation AI–based coronary calcium quantification approach to improve risk stratification among individuals with zero Agatston scores
This study evaluates Agatston 2.0, an AI–based approach designed to detect and quantify subtle coronary calcification in individuals traditionally classified as having a CAC score of zero. The work aims to refine the interpretation of the Power of Zero by identifying higher–risk individuals who may be missed by conventional threshold–based scoring.
6. Cardiometabolic phenotyping from coronary artery calcium scans predicts obstructive and high–risk plaques on coronary CT angiography within the Miami Heart Study
This study investigates whether AI–derived cardiometabolic phenotypes from CAC scans can predict obstructive and high–risk plaque findings on coronary CT angiography. The research supports the use of routine CAC imaging for broader plaque and cardiometabolic risk characterization.
7. Epicardial adipose tissue analysis identifies increased non–calcified plaque burden in non–obese individuals with low CAC scores within the Miami Heart Study
This study examines epicardial adipose tissue as an AI–derived marker of risk in non–obese individuals with low CAC scores. The work suggests that fat–based cardiovascular phenotyping may help identify patients with increased non–calcified plaque burden despite low traditional calcium scores.
8. Agatston 2.0: AI–derived calcium burden and plaque density profiling improve coronary heart disease risk stratification within CAC scores 1–99
This study evaluates how AI–derived calcium burden and plaque density can refine risk prediction among individuals with low–to–intermediate CAC scores from 1–99. The research suggests that not all CAC 1–99 scores carry the same risk and that AI–based plaque profiling may identify higher–risk individuals within this commonly encountered group.
9. A deep learning model for predicting aortic stenosis based on coronary artery calcium scans within MESA
This study applies deep learning to routine CAC scans to predict aortic stenosis risk. The research supports the potential of AI–CVD to expand cardiovascular screening beyond coronary artery disease and into valvular heart disease detection.
10. AI–derived LA volume index, LA/RA and LA/LV volume ratios from coronary artery calcium scans predict long–term atrial fibrillation and stroke
This study evaluates AI–derived left atrial volume index and chamber volume ratios from routine CAC scans for long–term prediction of atrial fibrillation and stroke. The findings support the role of opportunistic chamber volumetry in improving cardiovascular risk prediction beyond traditional scoring systems.

A Broad Scientific Program for Preventive Cardiology

Together, the 10 accepted studies demonstrate HeartLung.AI’s strategy of extracting multiple clinically meaningful risk markers from routine CT imaging. Rather than limiting CT analysis to a single finding, the AI–CVD platform is designed to identify cardiovascular and multisystem biomarkers from existing imaging, helping clinicians detect hidden risk earlier and at scale.
The ESC.26 program also reflects HeartLung.AI’s broader innovation strategy: using AI to transform existing CT imaging into an image–guided prevention platform that can support earlier intervention, improve patient engagement, and help health systems identify high–risk individuals before advanced disease develops.

Recognized Authors and Collaborators

• Amir Azimi, MD, AI-CVD Researcher, HeartLung AI, Houston, TX, USA.
• Kyle Atlas, MS, Research Scientist, HeartLung AI, Houston, TX, USA.
• Chenyu Zhang, MS, Software Engineer and AI-CVD Researcher, HeartLung AI, Houston, TX, USA.
• Anthony P. Reeves, PhD, Professor Emeritus, Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA.
• Seyed Reza Mirjalili, MD, AI-CVD Researcher, HeartLung AI, Houston, TX, USA.
• Mohammadhossein MozafaryBazargany, MD, AI-CVD Researcher, HeartLung AI, Houston, TX, USA.
• Amir Ghaffari Jolfayi, MD, AI-CVD Researcher, HeartLung AI, Houston, TX, USA.
• Ali Hashemi, AI-CVD Researcher, HeartLung AI, Houston, TX, USA.
• H. Mohammadi, MD, AI-CVD Researcher, HeartLung AI, Houston, TX, USA.
• Hamed Ghoshouni, MD, AI-CVD Researcher, HeartLung AI, Houston, TX, USA.
• Hamed Zarei, MD, Research Fellow, HeartLung AI, Houston, TX, USA.
• Zahi A. Fayad, PhD, Lucy G. Moses Professor of Medical Imaging and Bioengineering; Professor of Diagnostic, Molecular and Interventional Radiology and Medicine; Director, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
• David F. Yankelevitz, MD, Professor of Radiology and Director of the Lung Biopsy Service, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
• Nathan D. Wong, PhD, MPH, FACC, Professor of Medicine and Director, Heart Disease Prevention Program, Mary and Steve Wen Cardiovascular Division, University of California, Irvine, Irvine, CA, USA.
• Thomas Atlas, MD, Radiologist, Tustin Teleradiology, Tustin, CA, USA.
• Jakob Wasserthal, PhD, Medical Imaging AI Researcher, University Hospital Basel, Basel, Switzerland.
• Oren Mechanic, MD, MPH, MBA, Head of Innovation and Clinical Care, The Agatston Center for Preventive Medicine, Miami Beach, FL, USA.
• Rozemarijn Vliegenthart, MD, PhD, Professor of Cardiothoracic Imaging and Radiologist, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.
• Claudia I. Henschke, PhD, MD, Professor of Radiology and Head, Lung and Cardiac Screening Program, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
• Andrea D. Branch, PhD, Professor of Medicine, Division of Liver Diseases, and Associate Professor of Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
• Jamal S. Rana, MD, PhD, FACC, Chair of Medical Specialties and Interventional Services, Kaiser Permanente Oakland Medical Center, and Adjunct Investigator, Kaiser Permanente Division of Research, Oakland, CA, USA.
• Koen Nieman, MD, PhD, Professor of Cardiovascular Medicine and Radiology, Stanford University School of Medicine, Stanford, CA, USA.
• Jagat Narula, MD, PhD, Executive Vice President and Chief Academic Officer, UTHealth Houston, Houston, TX, USA; former Philip J. and Harriet L. Goodhart Chair of Cardiology and Professor of Medicine at the Icahn School of Medicine at Mount Sinai.
• Kim A. Williams Sr., MD, MACC, FAHA, MASNC, FESC, Professor and Chair, Department of Medicine, University of Louisville School of Medicine, Louisville, KY, USA.
• Prediman K. Shah, MD, Professor of Cardiology and Director, Atherosclerosis Prevention and Management Center, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
• Roxana Mehran, MD, Mount Sinai Professor in Cardiovascular Clinical Research and Outcomes; Professor of Medicine, Cardiology, and Population Health Science and Policy; Director, Interventional Cardiovascular Research and Clinical Trials, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
• Paolo Raggi, MD, Professor, Division of Cardiology, Department of Medicine, University of Alberta, Edmonton, Alberta, Canada.
• David J. Maron, MD, C.F. Rehnborg Professor of Medicine and Chief, Stanford Prevention Research Center, Stanford University School of Medicine, Stanford, CA, USA.
• Michael V. McConnell, MD, MSEE, Clinical Professor of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
• Robert A. Kloner, MD, PhD, Chief Science Officer and Director of Cardiovascular Research, Huntington Medical Research Institutes; Professor of Medicine, Keck School of Medicine of USC, Pasadena/Los Angeles, CA, USA.
• Matthew J. Budoff, MD, FACC, FAHA, Professor of Medicine, David Geffen School of Medicine at UCLA; Endowed Chair of Preventive Cardiology and Program Director, Cardiac CT, Harbor-UCLA / The Lundquist Institute, Torrance/Los Angeles, CA, USA.
• Arthur Agatston, MD, FACC, Chairman and CEO, The Agatston Center for Preventive Medicine, Miami Beach, FL, USA; creator of the Agatston Score.
• Morteza Naghavi, MD, Founder and President/CEO, HeartLung AI, Houston, TX, USA.

About HeartLung Technologies
HeartLung Technologies is a pioneer in AI–driven preventive imaging, focused on early detection of cardiovascular disease, lung cancer, COPD, osteoporosis, fatty liver disease, and other conditions detectable on CT scans. Its flagship platform, AI–CVD, transforms routine CT imaging into a scalable platform for comprehensive cardiovascular risk assessment and prevention.

Marlon Montes
HeartLung Technologies
+1 310-510-6004
contact@heartlung.ai
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