Machine learning of the spatio-temporal characteristics of echocardiographic deformation curves for infarct classification.
The aim of this study was to analyze the whole temporal profiles of the segmental deformation curves of the left ventricle (LV) and describe their interrelations to obtain more detailed information concerning global LV function in order to be able to identify abnormal changes in LV mechanics. The temporal characteristics of the segmental LV deformation curves were compactly described using an efficient decomposition into major patterns of variation through a statistical method, called Principal Component Analysis (PCA). In order to describe the spatial relations between the segmental traces, the PCA-derived temporal features of all LV segments were concatenated. The obtained set of features was then used to build an automatic classification system. The proposed methodology was applied to a group of 60 MRI-delayed enhancement confirmed infarct patients and 60 controls in order to detect myocardial infarction. An average classification accuracy of 87% with corresponding sensitivity and specificity rates of 89% and 85%, respectively was obtained by the proposed methodology applied on the strain rate curves. This classification performance was better than that obtained with the same methodology applied on the strain curves, reading of two expert cardiologists as well as comparative classification systems using only the spatial distribution of the end-systolic strain and peak-systolic strain rate values. This study shows the potential of machine learning in the field of cardiac deformation imaging where an efficient representation of the spatio-temporal characteristics of the segmental deformation curves allowed automatic classification of infarcted from control hearts with high accuracy.
Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium. mahdi.tabassian@kuleuven.be.|Department of Electrical, Electronic and Information Engineering, University of Bologna, Bologna, Italy. mahdi.tabassian@kuleuven.be.|Lab on Cardiovascular Imaging and Dynamics, UZ Herestraat 49, Box 7003, 3000, Leuven, Belgium. mahdi.tabassian@kuleuven.be.
Mahdi
http://orcid.org/0000-0002-9259-8059
M
Tabassian
Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium.|Department of Electrical, Electronic and Information Engineering, University of Bologna, Bologna, Italy.
Martino
M
Alessandrini
Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium.
Lieven
L
Herbots
Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium.
Oana
O
Mirea
Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium.
Efstathios D
ED
Pagourelias
Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium.
Ruta
R
Jasaityte
Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.
Jan
J
Engvall
Department of Electrical, Electronic and Information Engineering, University of Bologna, Bologna, Italy.
Luca
L
De Marchi
Department of Electrical, Electronic and Information Engineering, University of Bologna, Bologna, Italy.
Guido
G
Masetti
Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium.
Jan
J
D'hooge
D001331:Automation; D001696:Biomechanical Phenomena; D016022:Case-Control Studies; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D018618:Echocardiography, Doppler, Color / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging; D009200:Myocardial Contraction; D009203:Myocardial Infarction / Q000145:classification / Q000000981:diagnostic imaging* / Q000503:physiopathology; D015588:Observer Variation; D010363:Pattern Recognition, Automated; D011237:Predictive Value of Tests; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012720:Severity of Illness Index; D013997:Time Factors; D016277:Ventricular Function, Left
Automatic classification; Computer-aided diagnosis; Echocardiographic deformation curves; Principal component analysis; Spatio-temporal modeling of LV function