Machine learning‐based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy

Čikeš, Maja; Sanchez-Martinez, Sergio; Claggett, Brian; Duchateau, Nicolás; Piella, Gemma; Butakoff, Constantine; Pouleur, Anne‐Catherine; Knappe, Dorit; Biering‐Sørensen, Tor; Kutyifa, Valentina; Moss, Arthur J.; Steín, Kenneth M.; Solomon, Scott D.; Bijnens, Bart

doi:10.1002/ejhf.1333

Cited by 187 publications

(134 citation statements)

References 30 publications

Supporting

Mentioning

130

Contrasting

Unclassified

Order By: Relevance

“…quality control during CMR acquisition (41), high-resolution CMR study of cardiac remodeling in hypertension (42) and aortic stenosis (43), and echocardiographic differentiation of restrictive cardiomyopathy from constrictive pericarditis (44). Unsupervised ML analysis have provided new unbiased insights into cardiovascular pathologies such as by establishing subsets of patients likely to benefit from cardiac resynchronization therapy (45) and by agnostic identification of echocardiography derived patterns in patients with heart failure with preserved ejection fraction and controls (46). Traditional ML has also been used for prediction of outcomes such as hospital readmission due to heart failure (47), survival in pulmonary hypertension (48), and population-based cardiovascular risk prediction (49).…”

Section: Generalization and Replication Resultsmentioning

confidence: 99%

Artificial Intelligence for Cardiac Imaging-Genetics Research

Marvao

Dawes

O’Regan

2020

Front. Cardiovasc. Med.

View full text Add to dashboard Cite

Cardiovascular conditions remain the leading cause of mortality and morbidity worldwide, with genotype being a significant influence on disease risk. Cardiac imaging-genetics aims to identify and characterize the genetic variants that influence functional, physiological, and anatomical phenotypes derived from cardiovascular imaging. High-throughput DNA sequencing and genotyping have greatly accelerated genetic discovery, making variant interpretation one of the key challenges in contemporary clinical genetics. Heterogeneous, low-fidelity phenotyping and difficulties integrating and then analyzing large-scale genetic, imaging and clinical datasets using traditional statistical approaches have impeded process. Artificial intelligence (AI) methods, such as deep learning, are particularly suited to tackle the challenges of scalability and high dimensionality of data and show promise in the field of cardiac imaging-genetics. Here we review the current state of AI as applied to imaging-genetics research and discuss outstanding methodological challenges, as the field moves from pilot studies to mainstream applications, from one dimensional global descriptors to high-resolution models of whole-organ shape and function, from univariate to multivariate analysis and from candidate gene to genome-wide approaches. Finally, we consider the future directions and prospects of AI imaging-genetics for ultimately helping understand the genetic and environmental underpinnings of cardiovascular health and disease.

show abstract

Section: Generalization and Replication Resultsmentioning

confidence: 99%

Artificial Intelligence for Cardiac Imaging-Genetics Research

Marvao

Dawes

O’Regan

2020

Front. Cardiovasc. Med.

View full text Add to dashboard Cite

show abstract

“…It is known that IUGR fetuses show abnormal blood flow patterns in the fetal circulation detected by Doppler US [66,67] and also signs of longitudinal systolic dysfunction [58]. It was recently demonstrated that unsupervised ML algorithms using both echocardiographic (including myocardial strain traces) and clinical data can be used to find groups of similar patients within a heart failure cohort and identify individuals with a beneficial response to cardiac resynchronization therapy [68]. A similar approach integrating clinical and heterogeneous echocardiographic data could be implemented to improve the detection of IUGR fetuses, identify those at high risk of adverse perinatal outcomes, and aid clinicians in finding optimal treatment strategies.…”

Section: For Fetal Diagnosismentioning

confidence: 99%

Machine Learning in Fetal Cardiology: What to Expect

et al. 2020

Self Cite

View full text Add to dashboard Cite

In fetal cardiology, imaging (especially echocardiography) has demonstrated to help in the diagnosis and monitoring of fetuses with a compromised cardiovascular system potentially associated with several fetal conditions. Different ultrasound approaches are currently used to evaluate fetal cardiac structure and function, including conventional 2-D imaging and M-mode and tissue Doppler imaging among others. However, assessment of the fetal heart is still challenging mainly due to involuntary movements of the fetus, the small size of the heart, and the lack of expertise in fetal echocardiography of some sonographers. Therefore, the use of new technologies to improve the primary acquired images, to help extract measurements, or to aid in the diagnosis of cardiac abnormalities is of great importance for optimal assessment of the fetal heart. Machine leaning (ML) is a computer science discipline focused on teaching a computer to perform tasks with specific goals without explicitly programming the rules on how to perform this task. In this re-view we provide a brief overview on the potential of ML techniques to improve the evaluation of fetal cardiac function by optimizing image acquisition and quantification/segmentation, as well as aid in improving the prenatal diagnoses of fetal cardiac remodeling and abnormalities.

show abstract

“…They can represent the continuum of disease from normality while preserving the data structure (42). The unsupervised representation of populations is particularly interesting when existing labels are not fully trusted, as in heart failure with preserved ejection fraction (43,44) or when a supervised formulation of the clinical problem is uncertain, such as outcome from cardiac resynchronization therapy (45).…”

Section: Unsupervised Learningmentioning

confidence: 99%

Machine Learning Approaches for Myocardial Motion and Deformation Analysis

Duchateau

King

Craene

2020

Front. Cardiovasc. Med.

Self Cite

View full text Add to dashboard Cite

Information about myocardial motion and deformation is key to differentiate normal and abnormal conditions. With the advent of approaches relying on data rather than preconceived models, machine learning could either improve the robustness of motion quantification or reveal patterns of motion and deformation (rather than single parameters) that differentiate pathologies. We review machine learning strategies for extracting motion-related descriptors and analyzing such features among populations, keeping in mind constraints specific to the cardiac application.

show abstract

Machine learning‐based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy

Abstract: We tested the hypothesis that a machine learning (ML) algorithm utilizing both complex echocardiographic data and clinical parameters could be used to phenogroup a heart failure (HF) cohort and identify patients with beneficial response to cardiac resynchronization therapy (CRT).

Cited by 187 publications

References 30 publications

Artificial Intelligence for Cardiac Imaging-Genetics Research

Artificial Intelligence for Cardiac Imaging-Genetics Research

Machine Learning in Fetal Cardiology: What to Expect

Machine Learning Approaches for Myocardial Motion and Deformation Analysis

Contact Info

Product

Resources

About