Artificial intelligence driven plaque characterization and functional assessment from CCTA using OCT-based automation: A prospective study.
Authors
Affiliations (3)
Affiliations (3)
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China; The Key Laboratory of Myocardial Ischemia, Ministry of Education, Harbin, China.
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China; The Key Laboratory of Myocardial Ischemia, Ministry of Education, Harbin, China. Electronic address: [email protected].
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China; The Key Laboratory of Myocardial Ischemia, Ministry of Education, Harbin, China. Electronic address: [email protected].
Abstract
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. 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. 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). 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.