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.
This work was partially granted by the project DIMASDIA-COVID19, co-founded by Italy and POR FESR Regione Lombardia (2014–2020).
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Acknowledgement
We would like to thank the team of the Department of Cardiology of Luigi Sacco hospital, Echocardiography Laboratory in particular Maria Michela Caracciolo, Manfredo Cerchiello, Simone Colombo, Stefano De Vita, Alessandra Giavarini, Maria Isabella Tagliasacchi, for supplying us with the dataset.
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Bitetto, A. et al. (2021). Deep Learning Detection of Cardiac Akinesis in Echocardiograms. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12661. Springer, Cham. https://doi.org/10.1007/978-3-030-68763-2_38
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