Machine Learning Analysis of CMR images

The machine learning approaches may include radiomics feature extraction, strain analysis, and deriving missing information from MRI images. To address this challenge, a novel deep learning algorithm based on Generative Adversarial Networks (GANs) will be developed to synthesize sequencing images from existing OS-CMR data, reducing the need for time-consuming methods and making the process more cost-effective and time-saving. Overall, this explorative study aims to leverage the power of radiomics and machine learning to improve the diagnosis and management of cardiovascular diseases using cardiac MR images from the Courtois Cardiovascular Signature cohort from CMR images of patients and then impart the extracted information to a machine learning algorithm to classify different heart conditions. This aims to find & extract biomarkers from the non-invasive CMR images and weigh their use for classifying clinical outcomes in a machine learning algorithm. This process will allow an operator to synthesize this missing information from any CMR scan already acquired.