Automatic aortic valve area detection in echocardiography images using convolutional neural networks and U-net architecture for bicuspid aortic valve recognitionAutomatic aortic valve area detection in echocardiography images using convolutional neural networks and U-net architecture for bicuspid aortic valve recognition Πλήρης Δημοσίευση σε Συνέδριο Conference Full Paper 2023-05-112021enThis research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH—CREATE—INNOVATE (project code: T1EDK- 03505, e-MASS).Automatic methods for heart disease recognition are a promising asset in precise diagnosis and prevention of complications. Regarding bicuspid aortic valve, for which this field is still limited, accurate aortic valve detection would be an essential step in the procedure of using the most common testing method, echocardiography, to automatically detect this malformation. In this study, we propose using a convolutional neural network with U-net architecture for demarcating the aortic valve area in echocardiography images, as an initial step in automatic bicuspid aortic valve detection. Our model achieved a prediction accuracy of 97%, sensitivity 94%, specificity 98% and Intersection over Union 87%.http://creativecommons.org/licenses/by/4.0/2021 IEEE International Conference on Imaging Systems and Techniques Giannakaki Aikaterini-Antonia Γιαννακακη Αικατερινη-Αντωνια Moirogiorgou Konstantia Μοιρογιωργου Κωνσταντια Zervakis Michail Ζερβακης Μιχαηλ Anousakis-Vlachochristou Nikolaos Matsopoulos, George K Komporozos Christoforos Sourides Vasileios Katsimagklis Georgios Drakopoulou Maria Toutouzas, Konstantinos Avgeropoulou Catherine Androulakis Aristeidis Institute of Electrical and Electronics Engineers Echocardiography Convolutional neural networks U-net Aortic valve Bicuspid aortic valve