ArticleFrontiers in cardiovascular medicine2021
DeepStrain: A Deep Learning Workflow for the Automated Characterization of Cardiac Mechanics.
Article in Frontiers in cardiovascular medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
20 citing papers in PubMed.
- Automated cardiac strain estimation from short-axis DENSE and Cine MR images.Biomechanics and modeling in mechanobiology · 2026Article
- Deep learning-derived left ventricular circumferential strain for cardiac MRI diagnosis of acute myocarditis with preserved ejection fraction.The international journal of cardiovascular imaging · 2026Article
- Scalable Left Ventricular ROI Annotation for Stress Perfusion Cardiac MRI using Deep Learning with Visual Refinement.Journal of imaging informatics in medicine · 2026Article
- Deep Learning for Cardiac Wall Motion Analysis: A Review of Methods, Challenges, and Clinical Applications.Annals of biomedical engineering · 2026Review
- Multi-Frame Image Registration for Automated Ventricular Function Assessment in Single Breath-Hold Cine MRI Using Limited Labels.Magnetic resonance in medicine · 2026Article
- Reference Values for Myocardial Strain by Cardiac Magnetic Resonance Feature Tracking: Insights From Healthy Volunteers and Heart Failure Patients Using Caas MR.Reviews in cardiovascular medicine · 2026Article
- TagGen: Diffusion-based generative model for cardiac MR tagging super resolution.Magnetic resonance in medicine · 2025Article
- Automated Bi-Ventricular Segmentation and Regional Cardiac Wall Motion Analysis for Rat Models of Pulmonary Hypertension.Pulmonary circulation · 2025Article
- LaMoD: Latent Motion Diffusion Model For Myocardial Strain Generation.Shape in medical imaging : International Workshop, ShapeMI 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings. ShapeMI (Workshop) (2024 : Marrakech, Morocco) · 2025Article
- Improve myocardial strain estimation based on deformable groupwise registration with a locally low-rank dissimilarity metric.BMC medical imaging · 2024Article
- Stop moving: MR motion correction as an opportunity for artificial intelligence.Magma (New York, N.Y.) · 2024Review
- Deformation-encoding Deep Learning Transformer for High-Frame-Rate Cardiac Cine MRI.Radiology. Cardiothoracic imaging · 2024Article
- MULTIMODAL LEARNING TO IMPROVE CARDIAC LATE MECHANICAL ACTIVATION DETECTION FROM CINE MR IMAGES.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- Artificial Intelligence to derive aligned strain in cine CMR to detect patients with myocardial fibrosis: an open and scrutinizable approach.Research square · 2024Article
- StrainNet: Improved Myocardial Strain Analysis of Cine MRI by Deep Learning from DENSE.Radiology. Cardiothoracic imaging · 2023Article
- Deep Learning Synthetic Strain: Quantitative Assessment of Regional Myocardial Wall Motion at MRI.Radiology. Cardiothoracic imaging · 2023Article
- MRXCAT2.0: Synthesis of realistic numerical phantoms by combining left-ventricular shape learning, biophysical simulations and tissue texture generation.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2023Article
- Myocardial strain analysis of echocardiography based on deep learning.Frontiers in cardiovascular medicine · 2022Article
- DeepStrain Evidence of Asymptomatic Left Ventricular Diastolic and Systolic Dysfunction in Young Adults With Cardiac Risk Factors.Frontiers in cardiovascular medicine · 2022Article
- Cardiac MR: From Theory to Practice.Frontiers in cardiovascular medicine · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Myocardial strain analysis from cinematic magnetic resonance imaging (cine-MRI) data provides a more thorough characterization of cardiac mechanics than volumetric parameters such as left-ventricular ejection fraction, but sources of variation including segmentation and motion estimation have limited its wider clinical use. We designed and validated a fast, fully-automatic deep learning (DL) workflow to generate both volumetric parameters and strain measures from cine-MRI data consisting of segmentation and motion estimation convolutional neural networks. The final motion network design, loss function, and associated hyperparameters are the result of a thorough
Indexed as
Identifiers
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.