Development of an algorithm for automatic classification of right ventricle deformation patterns in arrhythmogenic right ventricular cardiomyopathy

• Published in: Echocardiography (Mount Kisco, N.Y.) Vol. 37; no. 5; pp. 698 - 705
• Main Authors: Groen, Marijn H. A., Bosman, Laurens P., Teske, Arco J., Mast, Thomas P., Taha, Karim, Van Slochteren, Frebus J., Cramer, Maarten J., Doevendans, Pieter A., Es, René
• Format: Journal Article
• Language: English
• Published: United States John Wiley and Sons Inc 01.05.2020
• Subjects: Algorithms; arrhythmogenic right ventricular cardiomyopathy; Arrhythmogenic Right Ventricular Dysplasia - diagnostic imaging; Arrhythmogenic Right Ventricular Dysplasia - genetics; classification; computer algorithm; Heart Ventricles - diagnostic imaging; Humans; Mutation; Original Investigation; Original Investigations; strain; strain; arrhythmogenic right ventricular cardiomyopathy; classification; computer algorithm
• ISSN: 0742-2822; 1540-8175; 1540-8175
• DOI: 10.1111/echo.14671

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.