Comparison of Machine Learning Computed Tomography-Based Fractional Flow Reserve and Coronary CT Angiography-Derived Plaque Characteristics with Invasive Resting Full-Cycle Ratio

Abstract

Background: The aim is to compare the machine learning-based coronary-computed tomography fractional flow reserve (CT-FFRML) and coronary-computed tomographic morphological plaque characteristics with the resting full-cycle ratio (RFRTM) as a novel invasive resting pressure-wire index for detecting hemodynamically significant coronary artery stenosis. Methods: In our single center study, patients with coronary artery disease (CAD) who had a clinically indicated coronary computed tomography angiography (cCTA) and subsequent invasive coronary angiography (ICA) with pressure wire-measurement were included. On-site prototype CT-FFRML software and on-site CT-plaque software were used to calculate the hemodynamic relevance of coronary stenosis. Results: We enrolled 33 patients (70% male, mean age 68 ± 12 years). On a per-lesion basis, the area under the receiver operating characteristic curve (AUC) of CT-FFRML (0.90) was higher than the AUCs of the morphological plaque characteristics length/minimal luminal diameter4 (LL/MLD4; 0.80), minimal luminal diameter (MLD; 0.77), remodeling index (RI; 0.76), degree of luminal diameter stenosis (0.75), and minimal luminal area (MLA; 0.75). Conclusion: CT-FFRML and morphological plaque characteristics show a significant correlation to detected hemodynamically significant coronary stenosis. Whole CT-FFRML had the best discriminatory power, using RFRTM as the reference standard.

The Impact of Artificial Intelligence on Cardiovascular Disease Diagnosis: A Review

“…These values indicate moderate to strong discriminatory power in identifying hemodynamically significant coronary artery stenoses. The findings of the research support the potential use of CT-FFRML as a non-invasive assessment method for suspected coronary artery stenosis while demonstrating its superiority over certain morphological plaque characteristics derived from regular anatomical CCTA in terms of discriminatory power and AUC values 30 . AI in Electrocardiogram Analysis: Electrocardiogram (ECG) is an essential tool for the detection and diagnosis of arrhythmias, myocardial ischemia, and other cardiac conditions 31 .…”

Radiogenomics and Artificial Intelligence Approaches Applied to Cardiac Computed Tomography Angiography and Cardiac Magnetic Resonance for Precision Medicine in Coronary Heart Disease: A Systematic Review

“…83 Other studies highlighted the significant predictive value of FFR ML algorithm for the assessment of lesion-specific ischemia and flow-limiting stenosis. 84–86 ML-based CT-FFR was also useful to detect coronary calcification with a superior diagnostic value over conventional CCTA analysis in vessels with high and low-intermediate AS. 87…”

Artificial Intelligence in Cardiology and Atherosclerosis in the Context of Precision Medicine: A Scoping Review

“…Seurat uses ML for cell classification. Also, other studies implement SPSS [ 94 , 110 ], PLINK [ 31 , 38 , 41 , 42 ], CARDIoGRAMplusC4D [ 26 , 34 , 37 ], or Ingenuity [ 20 , 23 , 112 ] that are strictly correlated with AI. The search biases are unavoidable as well and the authors are aware of this.…”