Intelligent detection method for carotid atherosclerotic plaques based on ultrasound imaging

The formation of carotid atherosclerotic plaques (CAP) is a major contributor to cardiovascular and cerebrovascular diseases. Early detection and accurate identification of plaques are crucial for reducing the risk of these conditions. However, traditional CAP detection methods rely heavily on manual analysis by clinical experts and specific imaging equipment, which limits their feasibility for large-scale screening and automated processing. Therefore, developing efficient and automated methods for CAP detection holds significant clinical value.

This paper presents an intelligent CAP detection approach based on ultrasound imaging. By extracting texture and morphological features from the images, such as gray-level co-occurrence matrix, gray-level run length matrix, invariant moments, and fractal dimension, we construct a machine learning model using random forest (RF) and other algorithms to achieve accurate and efficient CAP detection. We evaluated the model's performance on a dataset of 2,786 ultrasound images acquired from various devices, employing five-fold cross-validation.

The experimental results indicate that the proposed machine learning model achieves high CAP detection accuracy across multiple classification metrics. The optimal RF model demonstrated an accuracy of 80%, a precision of 82%, a recall of 89%, a specificity of 75%, and an F1 score of 85%. Furthermore, feature importance analysis allows us to identify the ultrasound image features most relevant to CAP detection, enhancing the interpretability of our model.

The machine learning model developed in this study effectively supports automated CAP detection and provides a reliable foundation for intelligent diagnosis using ultrasound imaging.