Aortic stenosis (AS) is one of the most common valvular heart disease, impacting in general the elderly population. The treatment is consistent in the implantation of a new aortic valve, which can be done through open-heart surgery, or using a catheter for the deployment of a new valve, like the transcatheter aortic valve implantation (TAVI). Although the TAVI procedure is indicated, there are not many public data about TAVI candidates or patients. in the first year after the procedure.
In this project our focus is on combining clinical data and clinical data with image data using machine learning approaches such as Convolutional Neural Networks in order to predict the post-procedure symptoms improvement and the mortality.
