ReviewFrontiers in cardiovascular medicine2019
Machine Learning Approaches for Myocardial Motion and Deformation Analysis.
Review in Frontiers in cardiovascular medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Cardiovascular MRI Feature-Tracking Strain Rate for Assessment of Diastolic Function.Radiology. Cardiothoracic imaging · 2025Article
- A personalized time-resolved 3D mesh generative model for unveiling normal heart dynamics.Nature machine intelligence · 2025Article
- A preoperative planning procedure of septal myectomy for hypertrophic obstructive cardiomyopathy using image-based computational fluid dynamics simulations and shape optimization.Scientific reports · 2024Article
- DeepMesh: Mesh-Based Cardiac Motion Tracking Using Deep Learning.IEEE transactions on medical imaging · 2024Article
- Automated segmentation of the left-ventricle from MRI with a fully convolutional network to investigate CTRCD in breast cancer patients.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Machine Learning in Cardio-Oncology: New Insights from an Emerging Discipline.Reviews in cardiovascular medicine · 2023Review
- Pixel-wise statistical analysis of myocardial injury in STEMI patients with delayed enhancement MRI.Frontiers in cardiovascular medicine · 2023Article
- Myocardial strain analysis of echocardiography based on deep learning.Frontiers in cardiovascular medicine · 2022Article
- Direct left-ventricular global longitudinal strain (GLS) computation with a fully convolutional network.Journal of biomechanics · 2022Article
- Applications of artificial intelligence in cardiovascular imaging.Nature reviews. Cardiology · 2021Review
- A deep-learning semantic segmentation approach to fully automated MRI-based left-ventricular deformation analysis in cardiotoxicity.Magnetic resonance imaging · 2021Article
- Validation of a deep-learning semantic segmentation approach to fully automate MRI-based left-ventricular deformation analysis in cardiotoxicity.The British journal of radiology · 2021Article
- Comprehensive enhanced methodology of an MRI-based automated left-ventricular chamber quantification algorithm and validation in chemotherapy-related cardiotoxicity.Journal of medical imaging (Bellingham, Wash.) · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Information about myocardial motion and deformation is key to differentiate normal and abnormal conditions. With the advent of approaches relying on data rather than pre-conceived 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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.