🧠🫀 AI + CCTA: from anatomy to actionable risk The CONFIRM2 registry takes a big step forward in showing what happens when AI-driven quantitative coronary CT angiography (CCTA) meets real-world clinical populations. 🔬 What’s new here? This isn’t just about detecting stenosis. 👉 AI enables automated, reproducible quantification of: ✔️ Total plaque burden ✔️ Non-calcified vs calcified plaque ✔️ High-risk plaque features Across large, multicenter datasets. 📊 Key insight Risk is not binary. Patients without “significant stenosis” can still carry: ⚠️ High plaque burden ⚠️ Adverse plaque phenotype And importantly: 👉 These features are strongly associated with outcomes, independent of traditional stenosis-based assessment. ⚡ Why this matters We’ve spent decades asking: “Is there a ≥50% stenosis?” But CONFIRM2 reinforces a shift toward: ➡️ Total disease quantification ➡️ Biology of plaque, not just lumen narrowing This aligns perfectly with the treat-to-plaque paradigm. ⚠️ The nuance AI is powerful—but: ✔️ Dependent on image quality ✔️ Sensitive to acquisition variability ✔️ Still requires clinical context 👉 Automation ≠ interpretation 🎯 Clinical implication AI-CCTA may: ✔ Improve risk stratification ✔ Identify patients missed by traditional metrics ✔ Guide earlier, more personalized prevention 🔮 Bottom line We are moving from: “Do you have a blockage?” ➡️ to “How much disease do you actually carry—and how dangerous is it?” AI doesn’t replace clinicians. But it’s starting to quantify what we’ve been underestimating for years.
