AI Detects Hidden Heart Disease Using Existing Scans Stored in Patient Records

Mass General Brigham researchers have developed a new AI tool in collaboration with the United States Department of Veterans Affairs (VA) to probe through previously collected CT scans and identify individuals with high coronary artery calcium (CAC) levels that place them at a greater risk for cardiovascular events. Their research, published in NEJM AI, showed the tool called AI-CAC had high accuracy and predictive value for future heart attacks and 10-year mortality. Their findings suggest that implementing such a tool widely may help clinicians assess their patients’ cardiovascular risk.

"Millions of chest CT scans are taken each year, often in healthy people, for example to screen for lung cancer. Our study shows that important information about cardiovascular risk is going unnoticed in these scans," said senior author Hugo Aerts, PhD, director of the Artificial Intelligence in Medicine (AIM) Program at Mass General Brigham. "Our study shows that AI has the potential to change how clinicians practice medicine and enable physicians to engage with patients earlier, before their heart disease advances to a cardiac event."

Chest CT scans can detect calcium deposits in the heart and arteries that increase the risk of a heart attack. The gold standard for quantifying CAC uses "gated" CT scans, that synchronize to the heartbeat to reduce motion during the scan. But most chest CT scans obtained for routine clinical purposes are "nongated."

The researchers recognized that CAC could still be detected on these nongated scans, which led them to develop AI-CAC, a deep learning algorithm to probe through the nongated scans and quantify CAC to help predict the risk of cardiovascular events. They trained the model on chest CT scans collected as part of the usual care of veterans across 98 VA medical centers and then tested AI-CAC’s performance on 8,052 CT scans to simulate CAC screening in routine imaging tests.

The researchers found the AI-CAC model was 89.4% accurate at determining whether a scan contained CAC or not. For those with CAC present, the model was 87.3% accurate at determining whether the score was higher or lower than 100, indicating a moderate cardiovascular risk. AI-CAC was also predictive of 10-year all-cause mortality - those with a CAC score of over 400 had a 3.49 times higher risk of death over a 10-year period than patients with a score of zero. Of the patients the model identified as having very high CAC scores (greater than 400), four cardiologists verified that almost all of them (99.2%) would benefit from lipid lowering therapy.

"At present, VA imaging systems contain millions of nongated chest CT scans that may have been taken for another purpose, around 50,000 gated studies. This presents an opportunity for AI-CAC to leverage routinely collected nongated scans for purposes of cardiovascular risk evaluation and to enhance care," said first author Raffi Hagopian, MD, a cardiologist and researcher in the Applied Innovations and Medical Informatics group at the VA Long Beach Healthcare System. "Using AI for tasks like CAC detection can help shift medicine from a reactive approach to the proactive prevention of disease, reducing long-term morbidity, mortality and healthcare costs."

Limitations to the study include the fact that the algorithm was developed on an exclusively veteran population. The team hopes to conduct future studies in the general population and test whether the tool can assess the impact of lipid-lowering medications on CAC scores.

Hagopian R, Strebel T, Bernatz S, Myers GA, Offerman E, Zuniga E, Kim CY, Ng AT, Iwaz JA, Nürnberg L, Singh SP.
AI Opportunistic Coronary Calcium Screening at Veterans Affairs Hospitals.
NEJM AI, 2025 May. 2025 doi: 10.1056/AIoa2400937

Most Popular Now

AI Catches One-Third of Interval Breast …

An AI algorithm for breast cancer screening has potential to enhance the performance of digital breast tomosynthesis (DBT), reducing interval cancers by up to one-third, according to a study published...

NHS National Rehabilitation Centre to De…

The new NHS National Rehabilitation Centre will deploy technology to help patients to maintain their independence as they recover from life-changing injuries and illnesses and regain quality of life. Airwave Healthcare...

AI Tool Accurately Detects Tumor Locatio…

An AI model trained to detect abnormalities on breast MR images accurately depicted tumor locations and outperformed benchmark models when tested in three different groups, according to a study published...

AI can Accelerate Search for More Effect…

Scientists have used an AI model to reassess the results of a completed clinical trial for an Alzheimer’s disease drug. They found the drug slowed cognitive decline by 46% in...

AI Accurately Classifies Pancreatic Cyst…

Artificial intelligence (AI) models such as ChatGPT are designed to rapidly process data. Using the AI ChatGPT-4 platform to extract and analyze specific data points from the Magnetic Resonance Imaging...

Free AI Tools can Help Doctors Read Medi…

A new study from the University of Colorado Anschutz Medical Campus shows that free, open-source artificial intelligence (AI) tools can help doctors report medical scans just as well as more...

Great plan: Now We need to Get Real abou…

The government's big plan for the 10 Year Health Plan for the NHS laid out a big role for delivery. However, the Highland Marketing advisory board felt the missing implementation...

Autonomous AI Agents in Healthcare

The use of large language models (LLMs) and other forms of generative AI (GenAI) in healthcare has surged in recent years, and many of these technologies are already applied in...

Can Amazon Alexa or Google Home Help Det…

Computer scientists at the University of Rochester have developed an AI-powered, speech-based screening tool that can help people assess whether they are showing signs of Parkinson’s disease, the fastest growing...

Researchers Create 'Virtual Scienti…

There may be a new artificial intelligence-driven tool to turbocharge scientific discovery: virtual labs. Modeled after a well-established Stanford School of Medicine research group, the virtual lab is complete with an...

From WebMD to AI Chatbots: How Innovatio…

A new research article published in the Journal of Participatory Medicine unveils how successive waves of digital technology innovation have empowered patients, fostering a more collaborative and responsive health care...

New AI Tool Accelerates mRNA-Based Treat…

A new artificial intelligence (AI) model can improve the process of drug and vaccine discovery by predicting how efficiently specific mRNA sequences will produce proteins, both generally and in various...