Artificial intelligence is transforming how we see the heart — turning static CT scans into living, breathing flow maps. In this episode, Dr. Ankitkumar Patel dives into how AI-derived CT-FFR bridges the long-standing gap between anatomy and physiology, helping clinicians move from “what the blockage looks like” to “what it actually does.”

We unpack why conventional CCTA often overcalls disease, how AI-based computational fluid dynamics (CFD) models learn from thousands of invasive FFR data points, and what this means for the future of noninvasive coronary diagnostics.

You’ll learn how deep learning models like CNNs (convolutional neural networks) segment vessels, how CFD approximates pressure drops, and why this combination is revolutionizing triage and treatment decisions. From early studies like DISCOVER-FLOW and NXT to modern real-world validation registries, we connect the dots between data and diagnosis.

This is the future of heart imaging — seeing function, not just form.

🎧 Key studies discussed:

• DISCOVER-FLOW (JACC 2011)

• DeFACTO (JACC 2012)

• NXT (EHJ 2014)

• PLATFORM (JACC 2016)

• ADVANCE Registry (JACC 2022)

• Deep Learning CT-FFR (CIRCULATION 2023)

• FFR_AI (Springer 2024)

• CT-FFR Variability Comparison (PMC 2024)

Myth of the Week:
“If your CT shows a blockage, you automatically need a stent.”
👉 Not true. AI-based CT-FFR shows us which lesions truly limit blood flow and which ones don’t.

Key Takeaways:

• AI makes anatomy functional — turning images into insights.

• CT-FFR reduces unnecessary caths and improves diagnostic confidence.

• The next frontier: on-site, deep learning–driven hemodynamics.