Background: We aimed to develop and validate an Artificial Intelligence (AI) model that leverages CCTA and optical coherence tomography (OCT) images for automated analysis of plaque characteristics and coronary function.
Methods: A total of 100 patients who underwent invasive coronary angiography, OCT, and CCTA before discharge were included in this study. The data were randomly divided into a training set (80 %) and a test set (20 %). The training set, comprising 21,471 tomography images, was used to train a deep-learning convolutional neural network. Subsequently, the AI model was integrated with flow reserve score calculation software developed by Ruixin Medical.
Results: The results from the test set demonstrated excellent agreement between the AI model and OCT analysis for calcified plaque (McNemar test, p = 0.683), non-calcified plaque (McNemar test, p = 0.752), mixed plaque (McNemar test, p = 1.000), and low-attenuation plaque (McNemar test, p = 1.000). Additionally, there was excellent agreement for deep learning-derived minimum lumen diameter (intraclass correlation coefficient [ICC] 0.91, p < 0.001), mean vessel diameter (ICC 0.88, p < 0.001), and percent diameter stenosis (ICC 0.82, p < 0.001). In diagnosing >50 % coronary stenosis, the diagnostic accuracy of the AI model surpassed that of conventional CCTA (AUC 0.98 vs. 0.76, p = 0.008). When compared with quantitative flow fraction, there was excellent agreement between QFR and AI-derived CT-FFR (ICC 0.745, p < 0.0001).
Conclusion: Our AI model effectively provides automated analysis of plaque characteristics from CCTA images, with the analysis results showing strong agreement with OCT findings. Moreover, the CT-FFR automatically analyzed by the AI model exhibits high consistency with QFR derived from coronary angiography.
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