Medical imaging and artificial intelligence to investigate neuro-cardiac pathologies and discover hidden relationships – a state of the art review

Cardiovascular and neurological diseases including their interactions are getting the attention of re- searchers and physicians. Both diseases often share common biomarkers, risk factors, and biological pathways. By now, researchers have confirmed that problems related to cardiovascular lead to neurolog- ical bad outcomes and vice versa. In addition, researchers have started to use machine/deep learning algorithms for better diagnosis. By now, few examples are published on little datasets consisting of com- puted tomography images, electrocardiograms, electroencephalograms, and so on, but most of the work is not done by artificial intelligence (AI). In this work, we reviewed a number of studies that have either used AI or manual computation with conventional techniques on different imaging modalities. From all studies, it is found that imaging modalities can support physicians in better diagnosis of neurological outcomes following cardiac events and/or diseases and vice versa. Moreover, AI driven technologies, like machine learning and deep learning, could be useful to delineate accurate models of diseases related to neuro-cardiac pathologies for predictions of consequent bad outcomes related to the different stages