A Composite Modeling Approach for the 3D Simulations and Medical Imaging of Cardiac ATTR Amyloidosis

Abstract

Mathematical Modeling and Machine Learning is playing a key role in applied mathematics. Machine learning has witnessed a tremendous amount of attention over the last few years. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. Imaging has played a variety of roles in the study of Cardiac Amyloid (CA) over the past four decades. We provide a short overview of Cardiac Amyloid and Medical imaging techniques used for diagnosis of amyloids in our heart as well as recent advances in techniques. We also working on different neural networks especially on CNN for diagnosis of cardiac amyloid with the aid of Resnet-50 network on MATLAB and tensor-flow on Python. This study aims to assist doctors in choosing an acceptable classification method for each patient's condition. The challenge for the future will be to availability to most efficient imaging data for this disease.