Cardiac AI: Unlocking Early Heart Disease Detection for Investors

Too often, the first time we diagnose coronary artery disease (CAD) is when the patient is having an MI. It’s a failure that shows just how badly we need to find subclinical atherosclerosis earlier and with more precision. Machine learning platforms are starting to make a real difference here, giving us a way to spot vulnerable plaque and physiological red flags before a patient ever feels symptoms. This letter is a quick breakdown of how these tools actually work, so clinicians can get a better handle on their technical guts and where they fit into practice.

The Algorithmic Frontier: Unpacking Early CAD Detection

To catch CAD early, you have to find the atherosclerotic burden and figure out if it matters functionally, which is tough when patients have no symptoms or just vague complaints. Our traditional diagnostic tools are good, but they’re held back by differences between readers and the fact that they often only give a clear answer when the disease is already advanced. Machine learning, especially deep learning running on our imaging scans, changes the game by pulling out subtle, quantitative data that we couldn’t get before. The whole idea is that these algorithms, trained on huge, expert-annotated image libraries, can spot disease patterns that are either invisible to us or would just take way too long to measure by hand.

Phenotyping Atherosclerosis: Cleerly’s Deep Learning Approach to Plaque Analysis

A big leap forward in early CAD detection is coming from platforms like Cleerly, which are applying deep learning to get a full quantitative workup from coronary computed tomography angiography (CCTA) scans. Under the hood, it’s all about advanced image segmentation and classification. The algorithms learn from massive datasets of CCTAs that have been painstakingly annotated by cardiologists and radiologists, teaching them to tell the difference between plaque types and measure them:

• Non-calcified plaque: We know these soft plaques are the more vulnerable ones, but they’re notoriously hard to eyeball and quantify reliably. Cleerly’s AI is built specifically to segment these out and give you a hard number for volume and characteristics.
• Calcified plaque: It’s less likely to rupture, but calcified plaque still adds to the total burden and causes stenosis, so it needs to be measured.
• Low-attenuation plaque: This is a particularly nasty subtype of non-calcified plaque that’s tied to higher risk for future events, and the AI is trained to find and quantify it specifically.
• Remodeling index: The AI also measures vessel remodeling, that process where the artery bulges outward to make room for plaque, which can hide the real severity of the disease until it’s very late in the game.

The data shows this approach works. Peer-reviewed accuracy for Cleerly’s FDA-cleared technology holds up well against invasive angio and IVUS, with high sensitivity and specificity for finding both obstructive and non-obstructive CAD. Their Cleerly ISCHEMIA software, which got its FDA 510(k) clearance in January 2024, gives a non-invasive FFR estimate and has performed with high diagnostic accuracy in trials. On top of that, recent multicenter trials report that using Cleerly’s analysis actually changes management decisions and makes clinicians more confident in their calls Peer-reviewed study on Cleerly’s diagnostic accuracy. Getting this kind of detailed plaque phenotype gives us a much sharper picture of a patient’s true burden and risk, taking us past a simple percentage stenosis to a real assessment of their arterial health.

Functional Assessment via AI: HeartFlow’s FFR-CT Model

Anatomy is one thing, but function is what drives our decisions about revascularization. HeartFlow was the first to really crack using machine learning to get fractional flow reserve (FFR) values from a standard CCTA (the FFR-CT). It’s a non-invasive way to get data that used to require a cardiac cath. The whole thing runs on a combination of computational fluid dynamics (CFD) and machine learning. Here’s how it works:

1. 3D Reconstruction: First, the AI builds a detailed 3D model of the patient’s coronary arteries directly from the CCTA images.
2. Boundary Conditions: Then it plugs in physiological conditions like aortic pressure and microvascular resistance, using a mix of patient-specific data and population-based averages.
3. CFD Simulation: With the model and conditions set, powerful CFD algorithms simulate exactly how blood flows and pressure changes through that specific coronary tree, predicting the pressure drop across any stenosis.
4. Machine Learning Refinement: Finally, machine learning models fine-tune the CFD results. These models have been trained on a massive library of cases with both CCTA and invasive FFR measurements, which is how the system learns to predict the FFR value with high accuracy without a wire.

In study after study, HeartFlow’s FFR-CT has shown high diagnostic accuracy when compared head-to-head with invasive FFR for spotting hemodynamically significant lesions Meta-analysis of FFR-CT diagnostic performance. The company also has FDA 510(k) clearance for its Plaque Analysis tool, with an updated algorithm cleared in September 2025 that gives a detailed breakdown of plaque type and volume. With validation from over 200 studies and more than 365,000 patients, the evidence base is deep. This is a big deal for early detection because it lets us find functionally important stenoses in people who might look low-risk on anatomy alone, helping us make much better calls about who really needs revascularization.

Beyond Detection: Viz.ai and the Broader AI Impact

Cleerly and HeartFlow are about early CAD, but the field of cardiovascular AI is much bigger. Take Viz.ai, which has had a huge impact on the acute care side. They got the first-ever De Novo FDA clearance back in February 2018 for an AI platform that triages and notifies teams about LVO strokes on CTA. It uses deep learning to rip through neuroimaging scans, spot large vessel occlusions, and alert the stroke team, which has been shown to cut treatment times. They’ve since expanded with FDA clearances for quantifying ICH (Viz ICH Plus, Feb 2024), spotting aneurysms (Viz ANEURYSM), quantifying subdural hematomas (Viz Subdural Plus, June 2025), and even detecting hypertrophic cardiomyopathy (Viz HCM, Aug 2023). So, while it’s not for chronic CAD screening, Viz.ai is a perfect example of AI’s power in acute situations, using rapid image analysis to optimize workflow when every second counts. The mechanism is all about fast anomaly detection, trained on mountains of stroke data to push the most critical cases to the top of the pile.

Building Clinical Trust: The Imperative of Mechanistic Understanding

If we’re going to use these AI platforms, we have to understand how they work. It’s that simple. Trust in an AI report can’t just come from a p-value on a spec sheet. It has to be built on knowing something about the imaging physics, the fluid dynamics, and the machine learning models that produce the result. Keeping up with this stuff is part of the job now, especially with how fast these tools are evolving. When you understand how an algorithm is analyzing a CCTA or simulating flow, you can be a critical user, you can question its outputs, figure out where it fits in your workflow, and actually use it to help patients. This kind of insight is what gives you real confidence that a tool can reliably spot early disease and helps you tell the difference between a validated platform and all the other noise in the cardiac AI market.

Methodology Note

This review is a synthesis of the peer-reviewed literature on non-invasive coronary imaging and the validation of AI in cardiovascular diagnostics. We focused on studies that lay out the technical specs and clinical performance for these platforms, sticking to the most reliable sources. Review of clinical validation standards for cardiovascular AI.