Deep neural network-based clustering of deformation curves reveals novel disease features in PLN pathogenic variant carriers.

Autor: Taha K; Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.; Netherlands Heart Institute, Utrecht, The Netherlands., van de Leur RR; Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.; Netherlands Heart Institute, Utrecht, The Netherlands., Vessies M; Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.; Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands., Mast TP; Department of Cardiology, Catharina Ziekenhuis, Eindhoven, The Netherlands., Cramer MJ; Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands., Cauwenberghs N; Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium., Verstraelen TE; Heart Center, Department of Cardiology, Amsterdam University Medical Center, Location Academic Medical Center, Amsterdam, The Netherlands., de Brouwer R; Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands., Doevendans PA; Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.; Netherlands Heart Institute, Utrecht, The Netherlands.; Central Military Hospital, Utrecht, The Netherlands., Wilde A; Heart Center, Department of Cardiology, Amsterdam University Medical Center, Location Academic Medical Center, Amsterdam, The Netherlands., Asselbergs FW; Heart Center, Department of Cardiology, Amsterdam University Medical Center, Location Academic Medical Center, Amsterdam, The Netherlands.; Health Data Research United Kingdom and Institute of Health Informatics, University College London, London, UK., van den Berg MP; Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands., D'hooge J; Laboratory on Cardiovascular Imaging and Dynamics, KU Leuven, Leuven, Belgium., Kuznetsova T; Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium., Teske AJ; Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands., van Es R; Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands. r.vanes-2@umcutrecht.nl.
Jazyk: angličtina
Zdroj: The international journal of cardiovascular imaging [Int J Cardiovasc Imaging] 2023 Nov; Vol. 39 (11), pp. 2149-2161. Date of Electronic Publication: 2023 Aug 11.
DOI: 10.1007/s10554-023-02924-9
Abstrakt: Echocardiographic deformation curves provide detailed information on myocardial function. Deep neural networks (DNNs) may enable automated detection of disease features in deformation curves, and improve the clinical assessment of these curves. We aimed to investigate whether an explainable DNN-based pipeline can be used to detect and visualize disease features in echocardiographic deformation curves of phospholamban (PLN) p.Arg14del variant carriers. A DNN was trained to discriminate PLN variant carriers (n = 278) from control subjects (n = 621) using raw deformation curves obtained by 2D-speckle tracking in the longitudinal axis. A visualization technique was used to identify the parts of these curves that were used by the DNN for classification. The PLN variant carriers were clustered according to the output of the visualization technique. The DNN showed excellent discriminatory performance (C-statistic 0.93 [95% CI 0.87-0.97]). We identified four clusters with PLN-associated disease features in the deformation curves. Two clusters showed previously described features: apical post-systolic shortening and reduced systolic strain. The two other clusters revealed novel features, both reflecting delayed relaxation. Additionally, a fifth cluster was identified containing variant carriers without disease features in the deformation curves, who were classified as controls by the DNN. This latter cluster had a very benign disease course regarding development of ventricular arrhythmias. Applying an explainable DNN-based pipeline to myocardial deformation curves enables automated detection and visualization of disease features. In PLN variant carriers, we discovered novel disease features which may improve individual risk stratification. Applying this approach to other diseases will further expand our knowledge on disease-specific deformation patterns. Overview of the deep neural network-based pipeline for feature detection in myocardial deformation curves. Firstly, phospholamban (PLN) p.Arg14del variant carriers and controls were selected and a deep neural network (DNN) was trained to detect the PLN variant carriers. Subsequently, a clustering-based approach was performed on the attention maps of the DNN, which revealed 4 distinct phenotypes of PLN variant carriers with different prognoses. Moreover, a cluster without features and a benign prognosis was detected.
(© 2023. The Author(s).)
Databáze: MEDLINE
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