Analysis methods of coronary artery intravascular images: A review

Automatic stent struts detection in optical coherence tomography based on a multiple attention convolutional model

Objective.Intravascular optical coherence tomography is a useful tool to assess stent adherence and dilation, thus guiding percutaneous coronary intervention and minimizing the risk of surgery. However, each pull-back OCT images may contain thousands of stent struts, which are tiny and dense, making manual stent labeling slow and costly for medical resources.Approach. This paper proposed a multiple attention convolutional model for automatic stent struts detection of OCT images. Multiple attention mechanisms were utilized to strengthen the feature extraction and feature fusion capabilities. In addition, to precisely detect tiny stent struts, the model integrated multiple anchor frames to predict targets in the output.Main results. The model was trained in 4625 frames OCT images of 37 patients and tested in 1156 frames OCT images of 9 patients, and achieved a precision of 0.9790 and a recall of 0.9541, which were significantly better than mainstream convolutional models. In terms of detection speed, the model achieved 25.2 ms per image. OCT images from different collection systems, collection times, and challenging scenarios were experimentally tested, and the model demonstrated stable robustness, achieving precision and recall higher than 0.9630. Meanwhile, clear 3D construction of the stent was achieved.Significance. In conclusion, the proposed model solves the problems of slow manual analysis and occupying a large amount of medical manpower resources. It enhances the detection efficiency of tiny and dense stent struts, thus facilitating the application of OCT quantitative analysis in real clinical scenarios.

In vivo detection of plaque erosion by intravascular optical coherence tomography using artificial intelligence

Plaque erosion is one of the most common underlying mechanisms for acute coronary syndrome (ACS). Optical coherence tomography (OCT) allows in vivo diagnosis of plaque erosion. However, challenge remains due to high inter- and intra-observer variability. We developed an artificial intelligence method based on deep learning for fully automated detection of plaque erosion in vivo, which achieved a recall of 0.800 ± 0.175, a precision of 0.734 ± 0.254, and an area under the precision-recall curve (AUC) of 0.707. Our proposed method is in good agreement with physicians, and can help improve the clinical diagnosis of plaque erosion and develop individualized treatment strategies for optimal management of ACS patients.