Development of an algorithm for automatic classification of right ventricle deformation patterns in arrhythmogenic right ventricular cardiomyopathy
Open Access
- 3 May 2020
- journal article
- research article
- Published by Wiley in Echocardiography
- Vol. 37 (5), 698-705
- https://doi.org/10.1111/echo.14671
Abstract
Background Different disease stages of arrhythmogenic right ventricular cardiomyopathy (ARVC) can be identified by right ventricle (RV) longitudinal deformation (strain) patterns. This requires assessment of the onset of shortening, (systolic) peak strain, and postsystolic index, which is time‐consuming and prone to inter‐ and intra‐observer variability. The aim of this study was to design and validate an algorithm to automatically classify RV deformation patterns. Methods We developed an algorithm based on specific local characteristics from the strain curves to detect the parameters required for classification. Determination of the onset of shortening by the algorithm was compared to manual determination by an experienced operator in a dataset containing 186 RV strain curves from 26 subjects carrying a pathogenic plakophilin‐2 (PKP2) mutation and 36 healthy subjects. Classification agreement between operator and algorithm was solely based on differences in onset shortening, as the remaining parameters required for classification of RV deformation patterns could be directly obtained from the strain curves. Results The median difference between the onset of shortening determined by the experienced operator and by the automatic detector was 5.3 ms [inter‐quartile range (IQR) 2.7–8.6 ms]. 96% of the differences were within 1 time frame. Both methods correlated significantly with ρ = 0.97 (P < .001). For 26 PKP2 mutation carriers, there was 100% agreement in classification between the algorithm and experienced operator. Conclusion The determination of the onset of shortening by the experienced operator was comparable to the algorithm. Our computer algorithm seems a promising method for the automatic classification of RV deformation patterns. The algorithm is publicly available at the MathWorks File Exchange.Keywords
This publication has 18 references indexed in Scilit:
- Prolonged Electromechanical Interval Unmasks Arrhythmogenic Right Ventricular Dysplasia/Cardiomyopathy in the Subclinical StageJournal of Cardiovascular Electrophysiology, 2016
- Clinical Presentation, Long-Term Follow-Up, and Outcomes of 1001 Arrhythmogenic Right Ventricular Dysplasia/Cardiomyopathy Patients and Family MembersCirculation: Cardiovascular Genetics, 2015
- Yield of Serial Evaluation in At-Risk Family Members of Patients With ARVD/CJournal of the American College of Cardiology, 2014
- Incremental Value of Cardiac Magnetic Resonance Imaging in Arrhythmic Risk Stratification of Arrhythmogenic Right Ventricular Dysplasia/Cardiomyopathy–Associated Desmosomal Mutation CarriersJournal of the American College of Cardiology, 2013
- Mutation‐Positive Arrhythmogenic Right Ventricular Dysplasia/Cardiomyopathy: The Triangle of Dysplasia DisplacedJournal of Cardiovascular Electrophysiology, 2013
- Right ventricular mechanical dispersion is related to malignant arrhythmias: a study of patients with arrhythmogenic right ventricular cardiomyopathy and subclinical right ventricular dysfunctionEuropean Heart Journal, 2011
- Diagnosis of Arrhythmogenic Right Ventricular Cardiomyopathy/DysplasiaCirculation, 2010
- Three-dimensional mapping of mechanical activation patterns, contractile dyssynchrony and dyscoordination by two-dimensional strain echocardiography: Rationale and design of a novel software toolboxCardiovascular Ultrasound, 2008
- Echocardiographic quantification of myocardial function using tissue deformation imaging, a guide to image acquisition and analysis using tissue Doppler and speckle trackingCardiovascular Ultrasound, 2007
- Right ventricular dysplasia: a report of 24 adult cases.Circulation, 1982