Key result
Machine learning-derived FFRCT yields high diagnostic accuracy for CAD with an AUC of 0.90.
Why the study?
To evaluate the diagnostic accuracy of machine learning-derived FFRCT for CAD against invasive coronary angiography-derived fractional flow reserve to support clinical translation.
Does machine learning-derived FFRCT accurately diagnose coronary artery disease compared to invasive FFR?
Meta-Analysis
Does machine learning-derived FFRCT accurately diagnose coronary artery disease compared to invasive FFR?
Machine learning-derived FFRCT shows high diagnostic accuracy for coronary artery disease compared to invasive FFR, supporting its potential as a noninvasive assessment tool.
Supports noninvasive CAD evaluation with ML-FFRCT; extends single-study data via Level 1 meta-analysis.
Background: This meta-analysis evaluates the diagnostic accuracy of machine learning-derived FFRCT (ML-FFRCT) for CAD, using invasive coronary angiography-derived fractional flow reserve (ICA-FFR) as the gold standard to provide evidence for clinical translation. Methods: We systematically searched PubMed and Embase for relevant studies. Study quality was assessed using QUADAS-2 in RevMan 5.3. Diagnostic performance was evaluated by pooling sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and the area under the curve (AUC) using Stata 14.0. Meta-regression and subgroup analyses were conducted based on the publication year, country, study design, sample source, and sample size. Results: The pooled SEN was 0.84 (95% CI: 0.79-0.87) and SPE was 0.83 (95% CI: 0.77-0.88). The PLR and NLR were 4.95 (95% CI: 3.58-6.84) and 0.20 (95% CI: 0.15-0.26), respectively. The DOR was 25.15 (95% CI: 14.87-42.52) and the AUC was 0.90 (95% CI: 0.87-0.93), indicating high diagnostic accuracy. Deeks' funnel plot revealed no significant publication bias. Conclusions: ML-FFRCT demonstrates high SEN and SPE in diagnosing CAD. These findings support its potential as a promising noninvasive tool for CAD assessment.
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Lian et al. (2025) conducted a meta-analysis in coronary artery disease. machine learning-derived FFRCT (ML-FFRCT) vs. invasive coronary angiography-derived fractional flow reserve (ICA-FFR) was evaluated on diagnostic accuracy (AUC 0.90, 95% CI 0.87-0.93). Machine learning-derived FFRCT demonstrated high diagnostic accuracy for coronary artery disease, with a pooled sensitivity of 0.84, specificity of 0.83, and an AUC of 0.90 (95% CI 0.87-0.93).
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