Objectives Fractional flow reserve (FFR) and instantaneous wave-Free Ratio (iFR) pressure measurements during invasive coronary angiography (ICA) are the gold standard for assessing vessel-specific ischemia. Artificial intelligence has emerged to compute FFR based on coronary computed tomography angiography (CCTA) images (CT-FFRAI). We assessed a CT-FFRAI deep learning model for the prediction of vessel-specific ischemia compared to invasive FFR/iFR measurements. Materials and methods We retrospectively selected 322 vessels from 275 patients at two centers who underwent CCTA and invasive FFR and/or iFR measurements during ICA within three months. A junior and senior radiologist at each center supervised vessel centerline-building to generate curvilinear reformats that were processed for CT-FFRAI binary outcomes (≤ 0.80 or > 0.80) prediction. Reliability for CT-FFRAI outcomes based on radiologists’ supervision was assessed with Cohen’s κ. Diagnostic values of CT-FFRAI were calculated using invasive FFR ≤ 0.80 (n = 224) and invasive iFR ≤ 0.89 (n = 238) as the gold standard. A multinomial logistic regression model, including all false-positive and false-negative cases, assessed the impact of patient- and CCTA-related factors on diagnostic values of CT-FFRAI. Results Concordance for CT-FFRAI binary outcomes was substantial (κ = 0.725, p < 0.001). Sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of CT-FFRAI in predicting vessel-specific ischemia on a per-vessel analysis, based on senior radiologists’ evaluations, were 85% (58/68) and 91% (78/86), 82% (128/156) and 78% (119/152), 67% (58/86) and 70% (78/111), 93% (128/138) and 94% (119/127), and 83% (186/224) and 83% (197/238), respectively. Coronary calcifications significantly reduced the diagnostic accuracy of CT-FFRAI (p < 0.001; OR, 1.002; 95% CI 1.001–1.003). Conclusion CT-FFRAI demonstrates high diagnostic performance in predicting vessel-specific coronary ischemia compared to invasive FFR and iFR. Coronary calcifications negatively affect specificity, suggesting that further improvements in spatial resolution could enhance accuracy. Key Points <Emphasis Type="BoldItalic">Question</Emphasis> How accurately can a new deep learning model (CT-FFRAI) assess vessel-specific ischemia from CCTA non-invasively compared to two validated pressure measurements during invasive coronary angiography? <Emphasis Type="BoldItalic">Findings</Emphasis> CT-FFRAI achieved high diagnostic accuracy in predicting vessel-specific ischemia, with high sensitivity and negative predictive value, independent of scanner type and radiologists’ experience. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> CT-FFRAI provides a non-invasive alternative to Fractional Flow Reserve and instantaneous wave-Free Ratio measurements during invasive coronary angiography for detecting vessel-specific ischemia, potentially reducing the need for invasive procedures, lowering healthcare costs, and improving patient safety. Graphical Abstract

Diagnostic performance of a coronary CT angiography-based deep learning model for the prediction of vessel-specific ischemia

期刊论文
出版模式：

开放获取
发表日期：

2025年10月11日

查看全文

Benjamin Peters,
Rolf Symons,
Sanad Oulkadi,
Annelies Van Breda,
Yoann Bataille,
Peter Kayaert,
Willem Dewilde,
Kenneth De Wilder,
Wouter M. A. Franssen,
Alain Nchimi,
Olivier Ghekiere

摘要

Objectives

Fractional flow reserve (FFR) and instantaneous wave-Free Ratio (iFR) pressure measurements during invasive coronary angiography (ICA) are the gold standard for assessing vessel-specific ischemia. Artificial intelligence has emerged to compute FFR based on coronary computed tomography angiography (CCTA) images (CT-FFR_AI). We assessed a CT-FFR_AI deep learning model for the prediction of vessel-specific ischemia compared to invasive FFR/iFR measurements.

Materials and methods

We retrospectively selected 322 vessels from 275 patients at two centers who underwent CCTA and invasive FFR and/or iFR measurements during ICA within three months. A junior and senior radiologist at each center supervised vessel centerline-building to generate curvilinear reformats that were processed for CT-FFR_AI binary outcomes (≤ 0.80 or > 0.80) prediction. Reliability for CT-FFR_AI outcomes based on radiologists’ supervision was assessed with Cohen’s κ. Diagnostic values of CT-FFR_AI were calculated using invasive FFR ≤ 0.80 (n = 224) and invasive iFR ≤ 0.89 (n = 238) as the gold standard. A multinomial logistic regression model, including all false-positive and false-negative cases, assessed the impact of patient- and CCTA-related factors on diagnostic values of CT-FFR_AI.

Results

Concordance for CT-FFR_AI binary outcomes was substantial (κ = 0.725, p < 0.001). Sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of CT-FFR_AI in predicting vessel-specific ischemia on a per-vessel analysis, based on senior radiologists’ evaluations, were 85% (58/68) and 91% (78/86), 82% (128/156) and 78% (119/152), 67% (58/86) and 70% (78/111), 93% (128/138) and 94% (119/127), and 83% (186/224) and 83% (197/238), respectively. Coronary calcifications significantly reduced the diagnostic accuracy of CT-FFR_AI (p < 0.001; OR, 1.002; 95% CI 1.001–1.003).

Conclusion

CT-FFR_AI demonstrates high diagnostic performance in predicting vessel-specific coronary ischemia compared to invasive FFR and iFR. Coronary calcifications negatively affect specificity, suggesting that further improvements in spatial resolution could enhance accuracy.

Key Points

Question How accurately can a new deep learning model (CT-FFR_AI) assess vessel-specific ischemia from CCTA non-invasively compared to two validated pressure measurements during invasive coronary angiography?

Findings CT-FFR_AI achieved high diagnostic accuracy in predicting vessel-specific ischemia, with high sensitivity and negative predictive value, independent of scanner type and radiologists’ experience.

Clinical relevance CT-FFR_AI provides a non-invasive alternative to Fractional Flow Reserve and instantaneous wave-Free Ratio measurements during invasive coronary angiography for detecting vessel-specific ischemia, potentially reducing the need for invasive procedures, lowering healthcare costs, and improving patient safety.

Graphical Abstract