{"id":33218,"date":"2024-03-12T17:41:14","date_gmt":"2024-03-12T16:41:14","guid":{"rendered":"https:\/\/www.teoresigroup.com\/?post_type=thesis&p=33218"},"modified":"2024-03-12T17:42:42","modified_gmt":"2024-03-12T16:42:42","slug":"echocardiographic-aortic-insufficiency-detection-using-3-dimensional-convolutional-neural-network-from-apical-4-chamber-views","status":"publish","type":"thesis","link":"https:\/\/teoresi.sixeleven.it\/de\/thesis\/echocardiographic-aortic-insufficiency-detection-using-3-dimensional-convolutional-neural-network-from-apical-4-chamber-views\/","title":{"rendered":"Echocardiographic aortic insufficiency detection using 3-dimensional convolutional neural network from apical 4-chamber views"},"content":{"rendered":"\n
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Abstract<\/h2>\n\n\n\n

This work proposes to use deep learning to automatically detect cases of aortic insufficiency from echocardiographic videos. Specifically, the use of a 3D CNN (Convolutional Neural Network) was proposed. First, we built our dataset from raw and unstructured data. A database was created containing all phenotypic parameters and echocardiographic measurements of the patients. In addition, all echocardiograms were labeled with the type of view to which they belonged, using a convolutional network. From these structured data, we were able to select 117 patients to form the dataset to be used for classification of aortic insufficiency. We developed a classifier based on R(2+1)D, which accepts video as input and provides in outuput the diagnosis of aortic insufficiency with an overall accuracy of 87.1%.<\/p>\n\n\n\n

Objective<\/h2>\n\n\n\n

Use of AI for identification of aortic valve insufficiency in echocardiography<\/p>\n\n\n\n

Methodologies<\/h2>\n\n\n\n

Bibliographic and experimental research<\/p>\n\n\n\n

Conclusions<\/h2>\n\n\n\n

The developed model achieved an overall accuracy of 87.1% and was able to correctly detect 80% of cases of patients with aortic insufficiency and 90% of cases of patients without aortic insufficiency. This study thus demonstrated how the use of a 3D CNN network was effective in identifying this pathology from echocardiographic videos showing the A4C view.<\/p>\n\n\n\n

Future developments<\/h2>\n\n\n\n

Extend the dataset, improve model performance and increase generalization of aortic regurgitation detection regardless of data source, identify other pathologies.<\/p>\n<\/div>\n<\/div>\n","protected":false},"featured_media":0,"template":"","university":[176],"thesis_type":[272,274],"keyword":[542,248,242,500],"class_list":["post-33218","thesis","type-thesis","status-publish","hentry","university-universita-degli-studi-di-napoli-federico-ii-de","thesis_type-artificial-intelligence-de","thesis_type-deep-learning-de","keyword-aortic-insufficiency","keyword-artificial-intelligence","keyword-deep-learning","keyword-echocardiography"],"yoast_head":"\nEchocardiographic aortic insufficiency detection using 3-dimensional convolutional neural network from apical 4-chamber views - Teoresi Group<\/title>\n<meta name=\"robots\" content=\"noindex, follow\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Echocardiographic aortic insufficiency detection using 3-dimensional convolutional neural network from apical 4-chamber views - Teoresi Group\" \/>\n<meta property=\"og:description\" content=\"Abstract This work proposes to use deep learning to automatically detect cases of aortic insufficiency from echocardiographic videos. 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