Your browser doesn't support javascript.
loading
Development of an algorithm for automatic classification of right ventricle deformation patterns in arrhythmogenic right ventricular cardiomyopathy.
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; van Es, René.
Afiliação
  • Groen MHA; Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands.
  • Bosman LP; Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands.
  • Teske AJ; Netherlands Heart Institute, Utrecht, The Netherlands.
  • Mast TP; Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands.
  • Taha K; Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands.
  • Van Slochteren FJ; Department of Cardiology, Catharina Hospital Eindhoven, Eindhoven, The Netherlands.
  • Cramer MJ; Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands.
  • Doevendans PA; Netherlands Heart Institute, Utrecht, The Netherlands.
  • van Es R; Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands.
Echocardiography ; 37(5): 698-705, 2020 05.
Article em En | MEDLINE | ID: mdl-32362023
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.
Assuntos
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Displasia Arritmogênica Ventricular Direita Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Echocardiography Assunto da revista: CARDIOLOGIA / DIAGNOSTICO POR IMAGEM Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Holanda

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Displasia Arritmogênica Ventricular Direita Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Echocardiography Assunto da revista: CARDIOLOGIA / DIAGNOSTICO POR IMAGEM Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Holanda