ReviewFrontiers in cardiovascular medicine2020
Artificial Intelligence in Cardiac Imaging With Statistical Atlases of Cardiac Anatomy.
Review in Frontiers in cardiovascular medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.
- The role of artificial intelligence in aortic valve stenosis: a bibliometric analysis.Frontiers in cardiovascular medicine · 2025Pooled it
- MorphiNet: A Graph Subdivision Network for Adaptive Bi-Ventricle Surface Reconstruction.IEEE transactions on medical imaging · 2026Article
- 3D cardiac shape analysis with variational point cloud autoencoders for myocardial infarction prediction and virtual heart synthesis.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2025Article
- Diagnostic performance of single-lead electrocardiograms for arterial hypertension diagnosis: a machine learning approach.Journal of human hypertension · 2025Observational
- Genetic basis of right and left ventricular heart shape.Nature communications · 2024Article
- CHeart: A Conditional Spatio-Temporal Generative Model for Cardiac Anatomy.IEEE transactions on medical imaging · 2024Article
- Machine learning used for simulation of MitraClip intervention: A proof-of-concept study.Frontiers in genetics · 2023Article
- Predicting one-year left ventricular mass index regression following transcatheter aortic valve replacement in patients with severe aortic stenosis: A new era is coming.Frontiers in cardiovascular medicine · 2023Article
- Le Cœur en Sabot: shape associations with adverse events in repaired tetralogy of Fallot.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2022Article
- Multi-Domain Variational Autoencoders for Combined Modeling of MRI-Based Biventricular Anatomy and ECG-Based Cardiac Electrophysiology.Frontiers in physiology · 2022Article
- Interpretable cardiac anatomy modeling using variational mesh autoencoders.Frontiers in cardiovascular medicine · 2022Article
- CT-Based Analysis of Left Ventricular Hemodynamics Using Statistical Shape Modeling and Computational Fluid Dynamics.Frontiers in cardiovascular medicine · 2022Article
- Detection of abnormal left ventricular geometry in patients without cardiovascular disease through machine learning: An ECG-based approach.Journal of clinical hypertension (Greenwich, Conn.) · 2021Article
- Atlas-based methods for efficient characterization of patient-specific ventricular activation patterns.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2021Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
In many cardiovascular pathologies, the shape and motion of the heart provide important clues to understanding the mechanisms of the disease and how it progresses over time. With the advent of large-scale cardiac data, statistical modeling of cardiac anatomy has become a powerful tool to provide automated, precise quantification of the status of patient-specific heart geometry with respect to reference populations. Powered by supervised or unsupervised machine learning algorithms, statistical cardiac shape analysis can be used to automatically identify and quantify the severity of heart diseases, to provide morphometric indices that are optimally associated with clinical factors, and to evaluate the likelihood of adverse outcomes. Recently, statistical cardiac atlases have been integrated with deep neural networks to enable anatomical consistency of cardiac segmentation, registration, and automated quality control. These combinations have already shown significant improvements in performance and avoid gross anatomical errors that could make the results unusable. This current trend is expected to grow in the near future. Here, we aim to provide a mini review highlighting recent advances in statistical atlasing of cardiac function in the context of artificial intelligence in cardiac imaging.
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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.