The RFCN ResNet-101 V2 neural network achieved an optimal accuracy-to-speed ratio for real-time coronary artery stenosis detection with a mean Average Precision of 0.94 and speed of 10 fps.
Do deep learning methods provide accurate and fast real-time coronary artery stenosis detection on invasive angiography?
Deep learning models, particularly RFCN ResNet-101 V2, demonstrate high accuracy and sufficient speed to enable real-time coronary artery stenosis detection during invasive angiography.
Invasive coronary angiography remains the gold standard for diagnosing coronary artery disease, which may be complicated by both, patient-specific anatomy and image quality. Deep learning techniques aimed at detecting coronary artery stenoses may facilitate the diagnosis. However, previous studies have failed to achieve superior accuracy and performance for real-time labeling. Our study is aimed at confirming the feasibility of real-time coronary artery stenosis detection using deep learning methods. To reach this goal we trained and tested eight promising detectors based on different neural network architectures (MobileNet, ResNet-50, ResNet-101, Inception ResNet, NASNet) using clinical angiography data of 100 patients. Three neural networks have demonstrated superior results. The network based on Faster-RCNN Inception ResNet V2 is the most accurate and it achieved the mean Average Precision of 0.95, F1-score 0.96 and the slowest prediction rate of 3 fps on the validation subset. The relatively lightweight SSD MobileNet V2 network proved itself as the fastest one with a low mAP of 0.83, F1-score of 0.80 and a mean prediction rate of 38 fps. The model based on RFCN ResNet-101 V2 has demonstrated an optimal accuracy-to-speed ratio. Its mAP makes up 0.94, F1-score 0.96 while the prediction speed is 10 fps. The resultant performance-accuracy balance of the modern neural networks has confirmed the feasibility of real-time coronary artery stenosis detection supporting the decision-making process of the Heart Team interpreting coronary angiography findings.
Данилов et al. (Wed,) conducted a other in Coronary artery disease (n=100). Deep learning neural networks (e.g., RFCN ResNet-101 V2) vs. Reference labeling by interventional cardiologist was evaluated on Mean Average Precision (mAP) for stenosis detection. The RFCN ResNet-101 V2 neural network achieved an optimal accuracy-to-speed ratio for real-time coronary artery stenosis detection with a mean Average Precision of 0.94 and speed of 10 fps.