P4383Machine learning approach for prdiction of postinfarction myocardial recovery using echocardiographic myocardial texture

• Published in: European heart journal Vol. 40; no. Supplement_1
• Main Authors: Michalski, B W, Skonieczka, S, Strzelecki, M, Simiera, M, Szymczyk, E, Wejner-Mik, P, Lipiec, P, Wierzbowska-Drabik, K, Kasprzak, J D
• Format: Journal Article
• Language: English
• Published: Oxford University Press 01.10.2019
• Subjects: Echocardiography: Technology
• ISSN: 0195-668X; 1522-9645
• DOI: 10.1093/eurheartj/ehz745.0788

Abstract Background Recovery of left ventricular (LV) function has significant prognostic significance after myocardial infarction (MI) but is challenging to predict. We applied machine learning algorithms (ML) to analyze echocardiographic myocardial texture for predicting long-term recovery of left ventricular myocardium Methods We used native and contrast-enhanced (Sonovue imaged with Contrast Perfusion Sequence, CPS) myocardial images acquired 7 days after reperfused ST-elevation MI from apical window recorded in 61 pts (19 females, age 59.7±11.9) with first ST-elevation MI treated with successful PCI. A custom software (MaZDa 4.6) was used for texture analysis. 299 image features were calculated for defined regions of interest in each image (9 features from histogram, 6 from gradient matrix, 20 from run length matrix, 220 from co-occurrence matrix, and 44 from wavelet transform). Up to 10 most reproducible parameters were selected based on low variation and, later, Fisher criterion with minimization of classification error along with average correlation coefficient. ML methods used to analyze textures included Multilayer perceptron neural network -MLP, support vector machines -SVM, Adaptive Boosting algorithm AdaBoost and library support vector machine. We defined recovery as: improvement of LV wall motion score index (WMSI), absence of remodeling defined as >8% increase in LV end-diastolic volume (LVEDV) and improvement >5% of LV ejection fraction % after 1 year. Results Effectiveness of tested methods was similar for predicting regional and local LV function evolution after one year. Results for native grayscale and red component of CPS myocardial perfusion images were comparable. Percent accuracy of prediction is shown in the table, with best result for WMSI. Accuracy of 1-yr predictions of LV function change ΔWMSI ΔEF ΔLVEDV native grey contrast red contrast grey native grey contrast red contrast grey native grey contrast red contrast grey Adaptive Boosting 78% 79% 78% 64% 64% 59% 61% 60% 60% Neural network 77% 79% 78% 63% 63% 58% 62% 59% 60% Library support vector machine 77% 79% 78% 63% 65% 64% 61% 60% 58% Support vectro machine 77% 79% 78% 64% 65% 59% 61% 60% 58% Conclusions Echocardiographic myocardial texture can be analyzed using machine learning approaches to predict global or regional recovery of myocardial function. Accuracy for predicting regional WMSI improvement was superior to prognosing LVEDV or EF change. Performance of different tools did not differ between native and contrast enhanced images.