Open AccessUtility of machine learning algorithms in assessing patients with a systemic right ventricle
Author(s) -
GerhardPaul Diller,
Sonya V. BabuNarayan,
Wei Li,
Jelena Radojevic,
Aleksander Kempny,
Anselm Uebing,
Konstantinos Dimopoulos,
Helmut Baumgartner,
Michael Α. Gatzoulis,
Stefan Orwat
Publication year - 2019
Publication title -
european heart journal. cardiovascular imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.576
H-Index - 92
eISSN - 2047-2412
pISSN - 2047-2404
DOI - 10.1093/ehjci/jey211
Subject(s) - ventricle , great arteries , parasternal line , medicine , convolutional neural network , algorithm , artificial intelligence , segmentation , medical diagnosis , machine learning , cardiology , computer science , deep learning , radiology
To investigate the utility of novel deep learning (DL) algorithms in recognizing transposition of the great arteries (TGA) after atrial switch procedure or congenitally corrected TGA (ccTGA) based on routine transthoracic echocardiograms. In addition, the ability of DL algorithms for delineation and segmentation of the systemic ventricle was evaluated.