Diagnostic performance of deep learning-based vascular extraction and stenosis detection technique for coronary artery disease

Abstract

Objective: To investigate the diagnostic performance of deep learning (DL)-based vascular extraction and stenosis detection technology in assessing coronary artery disease (CAD). Methods: The diagnostic performance of DL technology was evaluated by retrospective analysis of coronary computed tomography angiography in 124 suspected CAD patients, using invasive coronary angiography as reference standard. Lumen diameter stenosis ≥50% was considered obstructive, and the diagnostic performances were evaluated at per-patient, per-vessel and per-segment levels. The diagnostic performances between DL model and reader model were compared by the areas under the receiver operating characteristics curves (AUCs). Results: In patient-based analysis, AUC of 0.78 was obtained by DL model to detect obstructive CAD [sensitivity of 94%, specificity of 63%, positive predictive value of 94%, and negative predictive value of 59%], While AUC by reader model was 0.74 (sensitivity of 97%, specificity of 50%, positive predictive value of 93%, negative predictive value of 73%). In vessel-based analysis, the AUCs of DL model and reader model were 0.87 and 0.89 respectively. In segment-based analysis, the AUCs of 0.84 and 0.89 were obtained by DL model and reader model respectively. It took 0.47 min to analyze all segments per patient by DL model, which is significantly less than reader model (29.65 min) (p < 0.001). Conclusion: The DL technology can accurately and effectively identify obstructive CAD, with less time-consuming, and it could be a reliable diagnostic tool to detect CAD. Advances in knowledge: The DL technology has valuable prospect with the diagnostic ability to detect CAD.

The correlation of deep learning-based CAD-RADS evaluated by coronary computed tomography angiography with breast arterial calcification on mammography

“…The training set, validation set and test set were used for training model, tuning model and evaluating model performance respectively. This diagnostic performance of this system has been validated and reported in a recent published work 37 .…”

Coronary Artery Disease Detection by Machine Learning with Coronary Bifurcation Features

“…Remarkable progress have been made in applying different machine learning algorithms with medical features for detecting different diseases such as various types of cancer and cardiovascular diseases [12][13][14][25][26][27]. Abdar et al proposed a nested ensemble nu-support vector classification (NE-nu-SVC) model for the diagnosis of CAD [26].…”

“…The features selection is the first step and plays a crucial role in machine learning, which can impact the detection accuracy of the machine learning classifier [12]. Studies suggested that many features were clinically associated with CAD and varied with the degrees of diseases, including physiological-based features such as blood pressure, blood glucose, blood lipids, age, obesity degree, and overweight [1], signal-based features such as electrocardiogram (ECG) and phonocardiogram (PCG) signals [13], computer imaging-based features such as "gray value" [14], and morphological features such as diameter stenosis [15]. Although all these features were potentially used in machine learning for the detection of CAD, the performance of the machine learning classifier depended on the quality of features used.…”

Clinical Evaluation of Automatic Coronary Artery Disease Reporting and Data System (CAD-RADS TM) in Coronary CT Angiography Using Convolutional Neural Networks

“…The patients in the test set were under ICA examination with an interval of less than 30 days after CCTA procedure. We have previously reported the validation of our deep learning system [13,14]. Before training, the aorta, coronary artery and plaques were labeled on each image by a multi-layer manually annotation system consisting of multiple layers of trained graders.…”