Filip Loncaric1, Pablo-Miki Marti Castellote2, Sergio Sanchez-Martinez3, Dora Fabijanovic4, Loredana Nunno5, Maria Mimbrero5, Laura Sanchis5, Adelina Doltra5, Silvia Montserrat6, Maja Cikes4, Fatima Crispi7, Gema Piella2, Marta Sitges6, Bart Bijnens8. 1. Institute of Biomedical Research August Pi Sunyer, Barcelona, Spain. Electronic address: loncaric.filip@gmail.com. 2. Department of Information Technologies and Communication, Simulation, Imaging and Modelling for Biomedical Systems, Universitat Pompeu Fabra, Barcelona, Spain. 3. Institute of Biomedical Research August Pi Sunyer, Barcelona, Spain. 4. University of Zagreb School of Medicine, Department of Cardiovascular Diseases, University Hospital Centre Zagreb, Zagreb, Croatia. 5. Institute of Biomedical Research August Pi Sunyer, Barcelona, Spain; Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain. 6. Institute of Biomedical Research August Pi Sunyer, Barcelona, Spain; Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain; CIBERCV, Instituto de Salud Carlos III (CB16/11/00354), Madrid, Spain. 7. Institute of Biomedical Research August Pi Sunyer, Barcelona, Spain; Fetal Medicine Research Center, BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), Institut Clínic de Ginecologia Obstetricia i Neonatologia, Centre for Biomedical Research on Rare Diseases, Barcelona, Spain. 8. Institute of Biomedical Research August Pi Sunyer, Barcelona, Spain; ICREA, Barcelona, Spain.
Abstract
BACKGROUND: Echocardiography provides complex data on cardiac function that can be integrated into patterns of dysfunction related to the severity of cardiac disease. The aim of this study was to demonstrate the feasibility of applying machine learning (ML) to automate the integration of echocardiographic data from the whole cardiac cycle and to automatically recognize patterns in velocity profiles and deformation curves, allowing the identification of functional phenotypes. METHODS: Echocardiography was performed in 189 clinically managed patients with hypertension and 97 healthy individuals without hypertension. Speckle-tracking analysis of the left ventricle and atrium was performed, and deformation curves were extracted. Aortic and mitral blood pool pulsed-wave Doppler and mitral annular tissue pulsed-wave Doppler velocity profiles were obtained. These whole-cardiac cycle deformation and velocity curves were used as ML input. Unsupervised ML was used to create a representation of patients with hypertension in a virtual space in which patients are positioned on the basis of the similarity of their integrated whole-cardiac cycle echocardiography data. Regression methods were used to explore patterns of echocardiographic traces within this virtual ML-derived space, while clustering was used to define phenogroups. RESULTS: The algorithm captured different patterns in tissue and blood-pool velocity and deformation profiles and integrated the findings, yielding phenotypes related to normal cardiac function and others to advanced remodeling associated with pressure overload in hypertension. The addition of individuals without hypertension into the ML-derived space confirmed the interpretation of normal and remodeled phenotypes. CONCLUSIONS: ML-based pattern recognition is feasible from echocardiographic data obtained during the whole cardiac cycle. Automated algorithms can consistently capture patterns in velocity and deformation data and, on the basis of these patterns, group patients into interpretable, clinically comprehensive phenogroups that describe structural and functional remodeling. Automated pattern recognition may potentially aid interpretation of imaging data and diagnostic accuracy.
BACKGROUND: Echocardiography provides complex data on cardiac function that can be integrated into patterns of dysfunction related to the severity of cardiac disease. The aim of this study was to demonstrate the feasibility of applying machine learning (ML) to automate the integration of echocardiographic data from the whole cardiac cycle and to automatically recognize patterns in velocity profiles and deformation curves, allowing the identification of functional phenotypes. METHODS: Echocardiography was performed in 189 clinically managed patients with hypertension and 97 healthy individuals without hypertension. Speckle-tracking analysis of the left ventricle and atrium was performed, and deformation curves were extracted. Aortic and mitral blood pool pulsed-wave Doppler and mitral annular tissue pulsed-wave Doppler velocity profiles were obtained. These whole-cardiac cycle deformation and velocity curves were used as ML input. Unsupervised ML was used to create a representation of patients with hypertension in a virtual space in which patients are positioned on the basis of the similarity of their integrated whole-cardiac cycle echocardiography data. Regression methods were used to explore patterns of echocardiographic traces within this virtual ML-derived space, while clustering was used to define phenogroups. RESULTS: The algorithm captured different patterns in tissue and blood-pool velocity and deformation profiles and integrated the findings, yielding phenotypes related to normal cardiac function and others to advanced remodeling associated with pressure overload in hypertension. The addition of individuals without hypertension into the ML-derived space confirmed the interpretation of normal and remodeled phenotypes. CONCLUSIONS: ML-based pattern recognition is feasible from echocardiographic data obtained during the whole cardiac cycle. Automated algorithms can consistently capture patterns in velocity and deformation data and, on the basis of these patterns, group patients into interpretable, clinically comprehensive phenogroups that describe structural and functional remodeling. Automated pattern recognition may potentially aid interpretation of imaging data and diagnostic accuracy.
Authors: Sergio Sanchez-Martinez; Oscar Camara; Gemma Piella; Maja Cikes; Miguel Ángel González-Ballester; Marius Miron; Alfredo Vellido; Emilia Gómez; Alan G Fraser; Bart Bijnens Journal: Front Cardiovasc Med Date: 2022-01-04