HeartLung.AI Showcases 10 Scientific Presentations at ESC Congress 2026, Advancing AI-Powered Cardiovascular and Lung Imaging
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HeartLung.AI researchers presented a broad portfolio of scientific studies at ESC Congress 2026 in Munich, highlighting the potential of artificial intelligence to extract greater clinical value from routine CT imaging.
Munich, Germany — September 2026 — HeartLung.AI successfully concluded its participation at ESC Congress 2026, held August 28–31 in Munich, Germany, where members of the HeartLung.AI research team presented 10 scientific studies spanning cardiovascular and thoracic imaging, artificial intelligence, risk assessment, and opportunistic screening.
The presentations reflected HeartLung.AI’s ongoing mission to unlock more clinically meaningful information from existing medical imaging and demonstrate how AI-powered analysis of routine CT scans may support earlier detection, improved risk stratification, and more informed clinical decision-making.
Throughout the congress, HeartLung.AI researchers presented findings across multiple scientific sessions, engaging with clinicians, researchers, imaging specialists, and cardiovascular experts from around the world.
The strong interest, questions, and discussions surrounding the presentations underscored the growing role of artificial intelligence in transforming CT imaging from a primarily diagnostic tool into a richer source of quantitative information about cardiovascular and thoracic health.
Expanding the Clinical Value of Routine CT
A central theme across HeartLung.AI’s research at ESC Congress 2026 was a simple question:
How much more can we learn from a CT scan that has already been acquired?
Routine CT imaging contains a wealth of anatomical and quantitative information that may extend beyond the original reason for which the scan was performed. Advances in artificial intelligence make it increasingly possible to identify and quantify these additional imaging biomarkers automatically and at scale.
HeartLung.AI’s research presented at ESC.26 explored this opportunity across multiple areas of cardiovascular and thoracic health.
Among the topics presented were AI-based coronary artery calcium assessment, cardiovascular risk stratification, cardiac chamber analysis, structural and valvular disease, lung cancer screening, and the extraction of additional clinically relevant biomarkers from routine CT imaging.
Together, the studies demonstrate a broader approach to medical imaging: one scan can potentially provide many answers.
Advancing Coronary Calcium Assessment Beyond Traditional Scoring
One of the research areas highlighted at the congress focused on the next generation of coronary artery calcium assessment.
HeartLung.AI presented research related to Agatston-2.0, an AI-based approach designed to explore additional information contained within coronary calcium imaging and improve cardiovascular risk stratification beyond conventional calcium scoring alone.
The work investigated how advanced quantitative analysis of coronary calcification may help identify meaningful differences in cardiovascular risk, including among individuals whose traditional Agatston scores alone may not fully characterize their risk profile.
The research reflects HeartLung.AI’s broader effort to move cardiovascular imaging toward more comprehensive and individualized assessment using information already available within CT scans.
Revealing Cardiac Structure and Function From Existing Imaging
HeartLung.AI researchers also presented work exploring AI-derived measurements of cardiac chambers from coronary artery calcium scans.
By automatically analyzing structures such as the left atrium and cardiac chambers, AI may enable clinicians and researchers to identify additional imaging biomarkers associated with cardiovascular conditions and future clinical outcomes.
This approach could significantly expand the clinical information available from routine non-contrast CT examinations without requiring a separate dedicated cardiac imaging study.
The research presented at ESC.26 illustrates how AI-based segmentation and quantitative imaging can transform previously underutilized anatomical information into measurable cardiovascular phenotypes.
Extending Opportunistic Screening Beyond the Heart
HeartLung.AI’s scientific presentations also addressed one of the most important challenges in preventive medicine: identifying disease earlier using imaging that patients are already receiving.
Research presented during the congress examined opportunities for lung cancer screening and detection, highlighting important shortcomings in current screening pathways and the large volume of chest CT examinations performed outside formal lung cancer screening programs.
Many patients who may benefit from screening do not currently meet established eligibility criteria, are not up to date with recommended screening, or undergo CT examinations for unrelated clinical reasons.
AI-assisted analysis of existing CT scans creates an opportunity to identify clinically important findings that might otherwise remain unrecognized and potentially connect more patients with appropriate follow-up.
This concept is central to HeartLung.AI’s vision of opportunistic screening: extracting additional preventive health information from imaging that already exists.
From a Single Scan to a More Comprehensive Health Assessment
Across the 10 scientific presentations, a common theme emerged: CT imaging can provide substantially more information than a single measurement, diagnosis, or clinical indication.
Through automated segmentation, quantitative imaging, and AI-powered phenotyping, a routine CT examination may contain information related to:
Coronary artery calcium and cardiovascular risk
Cardiac chamber size and morphology
Structural and valvular abnormalities
Aortic health
Pulmonary findings and lung cancer risk
Body composition and other imaging biomarkers
Long-term cardiovascular and cardiometabolic risk
By bringing these measurements together, HeartLung.AI is working toward a future in which medical imaging contributes not only to diagnosing existing disease but also to identifying risk earlier and supporting preventive care.
Strong Scientific Engagement at ESC Congress 2026
The HeartLung.AI presentations generated meaningful engagement throughout ESC Congress, with attendees participating in discussions around the clinical implications of AI-powered CT analysis, opportunistic screening, cardiovascular prevention, and the integration of quantitative imaging into clinical workflows.
For the HeartLung.AI team, the congress provided an important opportunity to share research findings while also exchanging ideas with physicians, scientists, and innovators working across cardiovascular medicine and medical imaging.
The discussions reinforced a growing recognition that the future of medical imaging will increasingly involve extracting multiple clinically relevant insights from a single examination.
Rather than viewing each CT scan as serving only one clinical purpose, AI creates the possibility of transforming existing imaging into a broader platform for disease detection, risk assessment, and prevention.
One Scan, Many Answers
HeartLung.AI’s presence at ESC Congress 2026 represents another milestone in the company’s effort to advance AI-powered cardiovascular and thoracic imaging.
The 10 scientific presentations collectively demonstrate how artificial intelligence may help clinicians obtain greater value from existing CT imaging—without requiring patients to undergo additional scans simply to obtain many of these measurements.
This approach is captured in HeartLung.AI’s guiding concept:
One Scan, Many Answers.
By automatically identifying and quantifying clinically relevant biomarkers across the heart, lungs, vasculature, and surrounding anatomy, HeartLung.AI aims to help shift healthcare toward earlier detection and more proactive prevention.
“The scientific engagement we experienced at ESC Congress 2026 reinforces the importance of extracting more value from the medical imaging that already exists. Our goal is to help transform routine CT imaging into a more comprehensive tool for cardiovascular and thoracic risk assessment, enabling earlier insights that may ultimately support better patient care.”
— HeartLung.AI Research Team
Looking Ahead
Following ESC Congress 2026, HeartLung.AI will continue advancing its research collaborations and AI-powered imaging technologies across cardiovascular and thoracic health.
The company remains focused on developing scalable tools that can help clinicians analyze existing CT imaging more comprehensively, identify meaningful imaging biomarkers automatically, and translate those findings into actionable information.
As the volume of medical imaging continues to grow worldwide, HeartLung.AI believes the next major opportunity is not simply acquiring more scans—but learning more from every scan we already have.
Learn more about HeartLung.AI’s ESC Congress 2026 research and scientific
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