ArticleJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology2019
Machine learning for nuclear cardiology: The way forward.
Article in Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- AI Characterisation of Discordance Profiles Between Stress Electrocardiogram and Myocardial Tomoscintigraphy Using Random Forest XGBoost and SHAP.Medical devices (Auckland, N.Z.) · 2026Article
- Role of Deep Learning in Computed Tomography.Cureus · 2023Review
- An Automated Method for Artifical Intelligence Assisted Diagnosis of Active Aortitis Using Radiomic Analysis of FDG PET-CT Images.Biomolecules · 2023Article
- A methodological framework for AI-assisted diagnosis of active aortitis using radiomic analysis of FDG PET-CT images: Initial analysis.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2022Article
- Applications of Machine Learning in Cardiology.Cardiology and therapy · 2022Review
- Handling of derived imbalanced dataset using XGBoost for identification of pulmonary embolism-a non-cardiac cause of cardiac arrest.Medical & biological engineering & computing · 2022Article
- Artificial intelligence and machine learning in cardiovascular computed tomography.World journal of cardiology · 2021Review
- Diagnostic accuracy of stress-only myocardial perfusion SPECT improved by deep learning.European journal of nuclear medicine and molecular imaging · 2021Article
- Nuclear cardiac imaging between implementation and globalization: The key role of integration.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2021Article
- Article
- Cardiovascular Imaging and Intervention Through the Lens of Artificial Intelligence.Interventional cardiology (London, England) · 2021Review
- Review of cardiovascular imaging in the Journal of Nuclear Cardiology 2019: Single-photon emission computed tomography.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2020Review
- The Role of Artificial Intelligence in Echocardiography.Current cardiology reports · 2020Review
- Left ventricle segmentation in the era of deep learning.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2020Article
- The Role of Artificial Intelligence in Cardiovascular Imaging: State of the Art Review.Frontiers in cardiovascular medicine · 2020Review
- Artificial Intelligence (AI) and Cardiovascular Diseases: An Unexpected Alliance.Cardiology research and practice · 2020Review
- Cardiac magnetic resonance imaging: the future is bright.F1000Research · 2019Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
PubMed holds no abstract for this paper.
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.