ArticleNature communications2019
Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 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
41 citing papers in PubMed, 1 synthesis or guideline pooled it, 94 citations in OpenAlex.
- Development and application of artificial intelligence in cardiac imaging.The British journal of radiology · 2020Pooled it
- A Review on Registration Techniques for Cardiac Computed Tomography and Ultrasound Images.Bioengineering (Basel, Switzerland) · 2025Review
- Advanced and interpretable corneal staining assessment through fine grained knowledge distillation.NPJ digital medicine · 2025Article
- Artificial Intelligence in Cardiac Surgery: Transforming Outcomes and Shaping the Future.Clinics and practice · 2025Review
- Deep learning with noisy labels in medical prediction problems: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Article
- Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging.Nature medicine · 2024Article
- Artificial Intelligence in the Screening, Diagnosis, and Management of Aortic Stenosis.Reviews in cardiovascular medicine · 2024Review
- Article
- Deep Learning Synthetic Strain: Quantitative Assessment of Regional Myocardial Wall Motion at MRI.Radiology. Cardiothoracic imaging · 2023Article
- Deep learning-enabled analysis of medical images identifies cardiac sphericity as an early marker of cardiomyopathy and related outcomes.Med (New York, N.Y.) · 2023Article
- Diagnostic performance of deep learning-based vessel extraction and stenosis detection on coronary computed tomography angiography for coronary artery disease: a multi-reader multi-case study.La Radiologia medica · 2023Article
- Bimodal CNN for cardiovascular disease classification by co-training ECG grayscale images and scalograms.Scientific reports · 2023Article
- Improving classification of correct and incorrect protein-protein docking models by augmenting the training set.Bioinformatics advances · 2023Article
- Adaptation of Autoencoder for Sparsity Reduction From Clinical Notes Representation Learning.IEEE journal of translational engineering in health and medicine · 2023Article
- Synthesis of patient-specific multipoint 4D flow MRI data of turbulent aortic flow downstream of stenotic valves.Scientific reports · 2022Article
- Harnessing feature extraction capacities from a pre-trained convolutional neural network (VGG-16) for the unsupervised distinction of aortic outflow velocity profiles in patients with severe aortic stenosis.European heart journal. Digital health · 2022Article
- Strategies to Address the Lack of Labeled Data for Supervised Machine Learning Training With Electronic Health Records: Case Study for the Extraction of Symptoms From Clinical Notes.JMIR medical informatics · 2022Article
- Weakly Supervised Classification of Vital Sign Alerts as Real or Artifact.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- AI in health and medicine.Nature medicine · 2022Review
Corrections and comments
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Authors and funding
14 authors at 3 institutions in 1 country.
Funding
Abstract
Biomedical repositories such as the UK Biobank provide increasing access to prospectively collected cardiac imaging, however these data are unlabeled, which creates barriers to their use in supervised machine learning. We develop a weakly supervised deep learning model for classification of aortic valve malformations using up to 4,000 unlabeled cardiac MRI sequences. Instead of requiring highly curated training data, weak supervision relies on noisy heuristics defined by domain experts to programmatically generate large-scale, imperfect training labels. For aortic valve classification, models trained with imperfect labels substantially outperform a supervised model trained on hand-labeled MRIs. In an orthogonal validation experiment using health outcomes data, our model identifies individuals with a 1.8-fold increase in risk of a major adverse cardiac event. This work formalizes a deep learning baseline for aortic valve classification and outlines a general strategy for using weak supervision to train machine learning models using unlabeled medical images at scale.
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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.