Cardiovascular disease (CVD) is the leading cause of death in the western world and is particularly common in patients with chronic kidney disease (CKD). CVD may manifest as a heart attack, a stroke, heart failure or a sudden death, even in people who previously didn’t realise they had CVD. Although we have very good research to suggest that controlling risk factors for CVD reduces and even death, the ability of doctors and nurses to identify those patients who require such intensive treatment is hampered by the relatively inaccurate tools currently in use. The manner in which doctors predict a patients risk of CVD at present lacks the ability to provide accurate predictions on an individual patient level. This is particularly true for patients with CKD who do not have a bespoke risk calculator to reliably estimate this risk of CVD and also rely on blood tests to detect CKD. Artificial intelligence is an effective means of extracting novel insights from data and is increasingly being applied in Medicine in an attempt to help patients. There is potential to harness this technology for improving the prediction of diseases such as CVD and potentially provide an opportunity for people to be identified that may benefit from more intensive risk factor modification in order to reduce this risk over time. In particular when it comes to CVD, retinal image analysis provides promise in helping to identify patients at risk not only of future CVD but possibly also risk factors such as high blood pressure and chronic kidney disease that are present but unknown to the patient. In this project we propose to use retinal image data to investigate the association between these retinal images and the persons cardiovascular risk factors and kidney function.
Apoiadores / Parceiros / Patrocinadores
Ali Karaali is a Research Fellow in Trinity College Dublin, Ireland. He holds a PhD degree in Computer Science from Federal University of Rio Grande do Sul, Brazil. His expertise includes computer vision, machine learning and deep learning. He has several papers in top-tier journals (e.g. Transactions on Image Processing) and conferences (e.g. ICASSP, ICIP). His Ph.D. was mostly focusing on multiple aspects of defocus blur extraction, processing, and using the blur information in computer vision and image/video processing related applications. Currently, he works on Health Informatics. More specifically, his current research includes cardiovascular risk assessment and chronic kidney disease prediction using non-invasive state-of-the-art AI methods using retinal image data.