Plaque Detection in Carotid Artery using Image Processing - V S, Vikas; Chandrashekhar, Harshith (Journal of Neonatal Surgery, 2025)
Carotid artery plaque is a major contributor to ischemic strokes, making its early identification essential for effective treatment and prevention. The intricate structure of the carotid artery, combined with the subtle characteristics of early stage plaque, complicates timely diagnosis, often leading to treatment delays that negatively impact patient outcomes. This paper explores image processing methods and AI-driven approaches for the early identification and precise segmentation of carotid artery plaque. It specifically focuses on computer-aided detection (CAD) systems that utilize convolutional neural networks (CNNs) and U-Net-based frameworks within the realm of medical imaging, particularly with complex modalities like ultrasound, CT, and MRI scans. These techniques emphasize important diagnostic metrics such as the Dice Similarity Coefficient, sensitivity, and specificity, which are vital for assessing the accuracy of plaque identification and the overall reliability of diagnoses Models based on CNNs can detect plaque by differentiating it from adjacent arterial tissue using enhanced and pre-processed images, thus minimizing the limitations and biases associated with traditional imaging interpretations by radiologists. The U-Net architecture, tailored for medical image segmentation, effectively captures subtle variations in tissue texture within the carotid artery, making it beneficial for early detection. This model processes imaging data in a manner that reduces the chances of misdiagnosis and delays, alleviating the pressure on healthcare providers while significantly improving diagnostic accuracy.