ABSTRACT
Recent development in technology involves significant improvement in modelling, imaging, and simulation for cardiovascular disease (CVD) prediction. The main important analysis of CVD detection concerns understanding the complexity of the disease. This understanding leads to the development of advanced modelling and simulation tools for reducing the disease progression with proper knowledge on key factors of CVD. Development of advanced tools is based on data related to imaging, biological features, genomics, clinical, and nonclinical data. These advanced tools are noninvasive, reduce the cost and time in clinical trials, achieve optimum medical devices, and reduce the risk involved during implementation. Modelling helps in understanding the mechanism of CVD progression. Computational models help in simulating the behavior of CVD risk. The model can be developed to understand the hemodynamics of various diseases. Imaging is used for visualizing and assessing the CVD. There are various modalities, like magnetic resonance imaging (MRI), angiography, and echocardiography, for analyzing CVD. This advancement improves the accuracy of the early diagnosis of CVD. Modalities like ultrasound and optical coherence tomography (OCT) are used for analyzing the blood vessel for plaque detection. Simulation that involves virtual reality (VR) and augmented reality (AR) helps clinicians to practice the CVD analysis without risk for patients. Physicians can practice the surgeries before their actual operation, which helps in reducing the surgical error, and improve patient care. The technological development in modelling, imaging, and simulation helps transforming and improving the modern healthcare in CVD detection. Such advancement helps in improving the understanding of the nature of disease, in improving personalized medicine and decision-making of CVD in clinical practice. The advancement in technology improves diagnosis and prevents the progression of CVD leading to better quality of life.
