Artificial Intelligence-Derived Fractional Flow Reserve in Routine Clinical Practice: An International Multicenter Retrospective Study

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Background: Artificial intelligence-based fractional flow reserve (AI-FFR) incorporates a machine learning-based algorithm to derive FFR directly from angiography. Its accuracy compared with invasive FFR has not been assessed. Methods: AI-FFR was compared with wire-based FFR in patients with a single intermediate lesion (diameter stenosis ≥40% to <70%) at 5 centers in the United States and Israel. AI-FFR assessments were performed by core laboratory analysts blinded to invasive FFR results. Diagnostic performance metrics were calculated using an FFR threshold of ≤0.80. Results: = .41). AI-FFR showed a sensitivity of 90.2%, specificity of 94.9%, positive predictive value of 83.5%, negative predictive value of 97.1%, and overall diagnostic accuracy of 93.8%. The area under the receiver operating characteristic curve (AUC) was 0.93 (95% CI, 0.89-0.96). Among 151 lesions with wire-based FFR values in the borderline "gray zone" (0.75-0.85), AI-FFR demonstrated a diagnostic accuracy of 91.4% and an AUC of 0.91 (95% CI, 0.86-0.96). AI-FFR demonstrated high diagnostic accuracy across vessel types and lesion locations in men and women and between the US and Israeli cohorts. Conclusions: AI-FFR, an automated, machine learning-based tool, demonstrated high diagnostic accuracy compared with wire-based FFR. Its speed, simplicity, and independence from complex procedural steps may facilitate broader adoption during coronary angiography.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.