ArticleEuropean heart journal2021
Artificial intelligence in cardiovascular imaging-principles, expectations, and limitations.
Article in European heart journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed.
- Cardiovascular Risk Stratification and Surveillance in Cardio-Oncology: Mechanistic Foundations and Future Directions.Biomedicines · 2026Review
- Clinically Validated XAI for Calcified Plaque Segmentation in Coronary CT Angiography.Journal of clinical medicine · 2026Article
- Article
- A pipeline for developing deep learning prognostic prediction models in cardiac magnetic resonance image analysis.European heart journal. Digital health · 2026Article
- Using artificial intelligence to spot heart failure from ECGs: is it prime time?European heart journal · 2025Article
- Screening for severe coronary stenosis in patients with apparently normal electrocardiograms based on deep learning.BMC medical informatics and decision making · 2024Article
- Review
- Artificial intelligence-enhanced patient evaluation: bridging art and science.European heart journal · 2024Review
- Artificial Intelligence in the Differential Diagnosis of Cardiomyopathy Phenotypes.Diagnostics (Basel, Switzerland) · 2024Review
- Beyond ideals: why the (medical) AI industry needs to motivate behavioural change in line with fairness and transparency values, and how it can do it.AI & society · 2024Article
- Perivascular adipose tissue as a source of therapeutic targets and clinical biomarkers.European heart journal · 2023Review
- Artificial Intelligence Applications in Cardiovascular Magnetic Resonance Imaging: Are We on the Path to Avoiding the Administration of Contrast Media?Diagnostics (Basel, Switzerland) · 2023Review
- Artificial Intelligence as a Diagnostic Tool in Non-Invasive Imaging in the Assessment of Coronary Artery Disease.Medical sciences (Basel, Switzerland) · 2023Review
- Application of a deep learning algorithm to calcium scoring in myocardial perfusion imaging.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Article
- Advances in Diagnosis, Therapy, and Prognosis of Coronary Artery Disease Powered by Deep Learning Algorithms.JACC. Asia · 2023Review
- Strategies for Sudden Cardiac Death Prevention.Biomedicines · 2022Review
- Multimodality Advanced Cardiovascular and Molecular Imaging for Early Detection and Monitoring of Cancer Therapy-Associated Cardiotoxicity and the Role of Artificial Intelligence and Big Data.Frontiers in cardiovascular medicine · 2022Review
- Cardiac imaging techniques for the assessment of immune checkpoint inhibitor-induced cardiotoxicity and their potential clinical applications.American journal of cancer research · 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
2 authors.
Funding
Abstract
PubMed holds no abstract for this paper.
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.