Researchers have developed a novel artificial intelligence-powered method to detect lipid-rich plaques in coronary arteries using optical coherence tomography (OCT) images. These plaques, which are composed of fat and cholesterol, are a major contributor to unstable atherosclerotic lesions and are strongly associated with life-threatening cardiac events like heart attacks. By identifying such plaques early—before they rupture—this AI-driven approach could significantly improve risk assessment and enable timely interventions. OCT is a high-resolution imaging technique that provides detailed cross-sectional views of blood vessel walls, making it ideal for visualizing plaque composition. However, interpreting these images requires expertise, and detecting lipid-rich regions can be challenging even for experienced clinicians. The new AI system is trained to analyze OCT scans and automatically identify areas with high lipid content with greater accuracy and consistency than traditional methods. The model was developed using a large dataset of annotated OCT images from patients undergoing coronary angiography. It leverages deep learning algorithms to recognize subtle patterns and structural features associated with lipid deposits, such as low backscattering intensity and irregular plaque morphology. Early results show that the AI system achieves high sensitivity and specificity in distinguishing lipid-rich plaques from other types of arterial lesions. If validated in larger clinical trials, this technology could be integrated into routine cardiac imaging workflows, helping cardiologists detect vulnerable plaques earlier and more reliably. This could lead to more personalized treatment plans, including targeted medication or stenting, ultimately reducing the risk of sudden cardiac events. The approach also has the potential to support research into plaque progression and response to therapy, advancing the field of preventive cardiology.