What question did this study set out to answer?

The study aims to identify practical barriers to implementing AI-driven CT-FFR in real-world settings.

May 17, 2026Open Access

Practical Barriers to Real-World Implementation of Artificial Intelligence-Driven CT-Derived Fractional Flow Reserve

Key Points

The study aims to identify practical barriers to implementing AI-driven CT-FFR in real-world settings.
Used randomized trial design to evaluate AI-based CT-FFR effectiveness.
Assessed integration challenges within existing workflows for coronary disease diagnosis.
Considered the role of computational fluid dynamics in clinical application.
AI-based CT-FFR illustrated increased diagnostic accuracy compared to conventional methods.
Highlighting significant workflow improvements in coronary artery disease evaluation.
Identified multiple barriers to on-site implementation despite its potential benefits.

Abstract

Noninvasive coronary CT angiography (CCTA) has been firmly established as a first-line imaging modality for evaluating suspected coronary artery disease (CAD) 1,2.Nevertheless, the well-known discordance between the severity of anatomical stenosis and functional ischemia remains a central challenge, particularly for intermediate lesions 3.CT-derived fractional flow reserve (CT-FFR) extends cardiac CT from morphology to physiology and has consistently demonstrated improved diagnostic accuracy compared with CCTA alone 456.Computational fluid dynamics-based off-site CT-FFR (e.g., HeartFlow) has been the most extensively validated, and artificial intelligence (AI)-based on-site CT-FFR aims to enhance workflow integration and scalability 7.AI-based CT-FFR is widely anticipated as a scalable and workflow-integrated solution that offers rapid on-site analysis without additional scanning 5,8.In randomized

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