Objective: Unstable plaques can cause acute cardiovascular and cerebrovascular diseases. The stability and instability of plaque are related to the plaque echo status in ultrasound. Carotid videos provide detailed plaque information compared to static images. Ultrasound -based plaque echo classification is challenging due to noise, interference frames, small targets (plaques), and complex shape changes. Methods: This study proposes a Multi -dimensional Aggregation Network (MA -Net) guided by key features for plaque diagnosis based on carotid ultrasound video, which uses only video -level labels. MA -Net consists of Key -Feature (KF) and Temporal -Channel -Spatial (TCS) modules. The KF module learns the contribution of each frame to the classification at the feature level, adaptively infers the importance score of each frame, thereby reducing the influence of interference frames. The TCS module includes the Temporal -Channel (TC) and Temporal -Spatial (TS) sub -modules. In addition to studying the temporal dimension, it delves into the relationship between the channel and spatial dimensions. TC analyses the temporal dependencies among the channels and filters noise. Moreover, TS extracts features more accurately through the spatio-temporal information contained in the surrounding environment of the plaque. Results: The performance of MA -Net on the SHU-Ultrasound-Video-2020 dataset is better than that of the state-of-the-art models of video classification, showing at least a 5% increase in accuracy, with an accuracy rate of 87.36%.

The risk assessment of carotid plaque is strongly related to the plaque echo status in ultrasound. However, the echo classification of carotid plaques based on ultrasound remains challenging due to the changes in plaque shape and semantics, along with the complex vascular environment. This study proposed a framework for Classification of Plaque by Tracking Videos (CPTV). To the best of our knowledge, this is the first study on plaque classification by tracking ultrasound video rather than a sonographic view, which achieves accurate localization and stable echo classification. In the tracking task, Multi-scale Decoupling Tracking (MDTrack) module including Multi-scale Dilated Encoder (MDE) and Internal-Exterior Feature Decoupling (IEFD) was proposed to solve the problems caused by shape and semantic variations to achieve accurate plaque localization in ultrasound. In the classification task, the Tracking-assisted 3D Attention (T3D-Attention) module included recombination and 3D-Attention extracted plaque features and echo-related features in the vascular environment. The experiments demonstrated that the performance of CPTV is better than current mainstream tracking and classification methods, indicating that the tracking-assistance classification is a kind of enhancement method with high universality and stability in the plaque in ultrasound.

The uneven distribution of sample points is a common problem in medical datasets. How to improve the classification accuracy with these datasets remains to be solved. Based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm, a weight learning approach is proposed to utilize the density information of datasets for the accurate prediction of cardiovascular diseases (CVDs). The approach selects important features by the random forest (RF) algorithm, divides the sample points into three types and weights them using different values by weight learning based on the density. Thus, the constructed machine learning models that combine the original features and weight feature can learn density information, more effectively identify decision boundaries, and achieve better performance. Compared with conventional machine learning models, the cross-validation approach showed that the performance of machine learning models with weight learning could achieve improved accuracy by 3 percentage points with the Stroke dataset and more than 10 percentage points with the University of California, Irvine (UCI) dataset.