{"id":33219,"date":"2024-03-12T17:41:06","date_gmt":"2024-03-12T16:41:06","guid":{"rendered":"https:\/\/www.teoresigroup.com\/?post_type=thesis&#038;p=33219"},"modified":"2024-03-12T17:43:00","modified_gmt":"2024-03-12T16:43:00","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\/it\/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<div class=\"wp-block-columns align-center row sezione\">\n<div class=\"wp-block-column small-12 medium-10 large-8\">\n<h2 class=\"wp-block-heading has-text-align-center h5\">Abstract<\/h2>\n\n\n\n<p>Questo lavoro si propone di utilizzare il deep learning per rilevare automaticamente i casi di insufficienza aortica da video ecocardiografici. In particolare, \u00e8 stato proposto l&#8217;uso di una CNN 3D (Convolutional Neural Network). In primo luogo, abbiamo costruito il nostro set di dati a partire da dati grezzi e non strutturati. \u00c8 stato creato un database contenente tutti i parametri fenotipici e le misure ecocardiografiche dei pazienti. Tutti gli ecocardiogrammi, inoltre, sono stati etichettati con il tipo di vista a cui appartenevano, utilizzando una rete convoluzionale. Da questi dati strutturati, siamo riusciti a selezionare 117 pazienti per formare il set di dati da utilizzare per la classificazione dell&#8217;insufficienza aortica. Abbiamo sviluppato un classificatore basato su R(2+1)D, che accetta il video come input e fornisce in outuput la diagnosi dell&#8217;insufficienza aortica con una accuracy complessiva dell&#8217;87.1%.<\/p>\n\n\n\n<h2 class=\"wp-block-heading has-text-align-center\">Obiettivo<\/h2>\n\n\n\n<p>Utilizzo della AI per l\u2019identificazione di insufficienza valvolare aortica in ecocardiografie<\/p>\n\n\n\n<h2 class=\"wp-block-heading has-text-align-center h5\">Metodologia di ricerca<\/h2>\n\n\n\n<p>Ricerca bibliografica e sperimentale<\/p>\n\n\n\n<h2 class=\"wp-block-heading has-text-align-center h5\">Conclusions<\/h2>\n\n\n\n<p>Il modello sviluppato ha raggiunto un&#8217;accuratezza complessiva dell&#8217;87,1% ed \u00e8 stato in grado di rilevare correttamente l&#8217;80% dei casi di pazienti con insufficienza aortica e il 90% dei casi di pazienti senza insufficienza aortica. Questo studio ha dunque dimostrato come l\u2019uso di una rete CNN 3D sia stato efficace nell\u2019identificare questa patologia dai video ecocardiografici che mostrano la vista A4C.<\/p>\n\n\n\n<h2 class=\"wp-block-heading has-text-align-center h5\">Future developments<\/h2>\n\n\n\n<p>Estendere il dataset, migliorare le prestazioni del modello e aumentare la generalizzazione del rilevamento del rigurgito aortico, indipendentemente dalla fonte dei dati, individuare altre patologie.<\/p>\n<\/div>\n<\/div>\n","protected":false},"featured_media":0,"template":"","university":[177],"thesis_type":[273,275],"keyword":[248,242,500,543],"class_list":["post-33219","thesis","type-thesis","status-publish","hentry","university-universita-degli-studi-di-napoli-federico-ii-it","thesis_type-artificial-intelligence-it","thesis_type-deep-learning-it","keyword-artificial-intelligence","keyword-deep-learning","keyword-echocardiography","keyword-insufficienza-aortica"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Echocardiographic 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=\"it_IT\" \/>\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 Questo lavoro si propone di utilizzare il deep learning per rilevare automaticamente i casi di insufficienza aortica da video ecocardiografici. 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