Abstract
Heart diseases are still among the main causes of death in the world population. The use of tools able to discriminate early this type of problem, even by non-specialized medical personnel on an outpatient basis, would put a decrease in health pressure on hospital centers and a better patient prognosis. This paper focuses on the problem of cardiac akinesis, a condition attributable to a very large number of pathologies, and a possible serious complication for SARS-Covid19 patients. In particular, we considered echocardiographic images of both akinetic and healthy patients. The dataset, containing echocardiograms of around 700 patients, has been supplied by Sacco hospital of Milan (Italy). We implemented a modified ResNet34 architecture and we tested the model under various combinations of parameters. The final best performing model was able to achieve a F1-score of 0.91 in the binary classification Akinetic vs. Normokinetic.
