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Published on: August 9, 2024
The diagnostic performance of machine learning-based FFRCT for coronary artery disease: A meta-analysis
Rui Lian1,2, Xiangmin Zhang3,4,5,6
1Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China.
Insights
Machine learning-derived fractional flow reserve computed tomography (ML-FFRCT) shows high accuracy in diagnosing coronary artery disease (CAD). This noninvasive tool demonstrates significant potential for clinical assessment of CAD.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Coronary artery disease (CAD) is a significant global health concern.
- Accurate noninvasive diagnostic tools for CAD are crucial for timely intervention.
- Fractional flow reserve computed tomography (FFRCT) offers a potential noninvasive approach to assess CAD severity.
Purpose of the Study:
- To evaluate the diagnostic accuracy of machine learning-derived FFRCT (ML-FFRCT) for CAD.
- To compare ML-FFRCT performance against invasive coronary angiography-derived FFR (ICA-FFR) as the gold standard.
- To provide evidence supporting the clinical translation of ML-FFRCT.
Main Methods:
- Systematic literature search of PubMed and Embase databases.
- Quality assessment of included studies using QUADAS-2.
- Meta-analysis of diagnostic performance metrics including sensitivity, specificity, likelihood ratios, and AUC.
- Meta-regression and subgroup analyses to explore heterogeneity.
Main Results:
- Pooled sensitivity (SEN) of 0.84 (95% CI: 0.79-0.87) and specificity (SPE) of 0.83 (95% CI: 0.77-0.88).
- Positive likelihood ratio (PLR) of 4.95 and negative likelihood ratio (NLR) of 0.20.
- Diagnostic odds ratio (DOR) of 25.15 and area under the curve (AUC) of 0.90 (95% CI: 0.87-0.93), indicating high diagnostic accuracy.
- No significant publication bias detected.
Conclusions:
- ML-FFRCT demonstrates high diagnostic accuracy for CAD.
- The findings support ML-FFRCT as a promising noninvasive tool for CAD assessment.
- Further clinical validation is warranted to facilitate widespread adoption.
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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