ReviewFrontiers in cardiovascular medicine2020
Mini Review: Deep Learning for Atrial Segmentation From Late Gadolinium-Enhanced MRIs.
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 17 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
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 48 citations in OpenAlex.
- Medical image analysis on left atrial LGE MRI for atrial fibrillation studies: A review.Medical image analysis · 2022Pooled it
- Multimodal Characterization of Atrial Fibrillation: From Patient-Specific Anatomy and Electrophysiology to Standardized Atrial Mapping.Tomography (Ann Arbor, Mich.) · 2026Review
- Atrial fibrosis in atrial fibrillation: Mechanisms, mapping techniques and clinical applications.The Journal of physiology · 2026Review
- Contrast-Free Myocardial Infarction Segmentation with Attention U-Net.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence in Cardiac Electrophysiology: A Clinically Oriented Review with Engineering Primers.Bioengineering (Basel, Switzerland) · 2025Review
- Automatic Bi-Atrial Segmentation and Biomarker Extraction from Late Gadolinium-Enhanced MRI Using Deep Learning.Expert systems with applications · 2025Article
- Deep learning based automated left atrial segmentation and flow quantification of real time phase contrast MRI in patients with atrial fibrillation.The international journal of cardiovascular imaging · 2025Article
- The beating heart: artificial intelligence for cardiovascular application in the clinic.Magma (New York, N.Y.) · 2024Review
- Article
- Successes and challenges of artificial intelligence in cardiology.Frontiers in digital health · 2023Review
- Convolutional Neural Networks for Mechanistic Driver Detection in Atrial Fibrillation.International journal of molecular sciences · 2022Article
- Whole-Heart High-Resolution Late Gadolinium Enhancement: Techniques and Clinical Applications.Journal of magnetic resonance imaging : JMRI · 2022Review
- Current Advancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review Study.Diagnostics (Basel, Switzerland) · 2022Review
- Article
- Understanding PITX2-Dependent Atrial Fibrillation Mechanisms through Computational Models.International journal of molecular sciences · 2021Review
- Supervised Domain Adaptation for Automated Semantic Segmentation of the Atrial Cavity.Entropy (Basel, Switzerland) · 2021Article
- Recent Advances in Fibrosis and Scar Segmentation From Cardiac MRI: A State-of-the-Art Review and Future Perspectives.Frontiers in physiology · 2021Review
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Segmentation and 3D reconstruction of the human atria is of crucial importance for precise diagnosis and treatment of atrial fibrillation, the most common cardiac arrhythmia. However, the current manual segmentation of the atria from medical images is a time-consuming, labor-intensive, and error-prone process. The recent emergence of artificial intelligence, particularly deep learning, provides an alternative solution to the traditional methods that fail to accurately segment atrial structures from clinical images. This has been illustrated during the recent 2018 Atrial Segmentation Challenge for which most of the challengers developed deep learning approaches for atrial segmentation, reaching high accuracy (>90% Dice score). However, as significant discrepancies exist between the approaches developed, many important questions remain unanswered, such as which deep learning architectures and methods to ensure reliability while achieving the best performance. In this paper, we conduct an in-depth review of the current state-of-the-art of deep learning approaches for atrial segmentation from late gadolinium-enhanced MRIs, and provide critical insights for overcoming the main hindrances faced in this task.
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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.