ReviewCardiovascular research2020
Artificial intelligence in medical imaging: A radiomic guide to precision phenotyping of cardiovascular disease.
Review in Cardiovascular research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 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
40 citing papers in PubMed.
- Radiomics in Medical Imaging: Methods, Applications, and Challenges.Journal of imaging · 2026Review
- Reimagining healthcare education through nurturing AI-driven innovation.BMC medical education · 2025Review
- Arterial phase CT radiomics for non-invasive prediction of Ki-67 proliferation index in pancreatic solid pseudopapillary neoplasms.Abdominal radiology (New York) · 2025Article
- Challenges and advances in the management of inflammation in atherosclerosis.Journal of advanced research · 2025Review
- Review
- Precision phenotyping from routine laboratory parameters for out of hospital survival prediction in an all comers prospective PCI registry.Scientific reports · 2024Article
- Review
- Diagnosis of perimenopausal coronary heart disease patients using radiomics signature of pericoronary adipose tissue based on coronary computed tomography angiography.Scientific reports · 2024Article
- Advances in Clinical Imaging of Vascular Inflammation: A State-of-the-Art Review.JACC. Basic to translational science · 2024Review
- Large-Scale assessment of ChatGPT's performance in benign and malignant bone tumors imaging report diagnosis and its potential for clinical applications.Journal of bone oncology · 2024Article
- Identification of patients with unstable angina based on coronary CT angiography: the application of pericoronary adipose tissue radiomics.Frontiers in cardiovascular medicine · 2024Article
- Artificial Intelligence in Cardiology and Atherosclerosis in the Context of Precision Medicine: A Scoping Review.Applied bionics and biomechanics · 2024Article
- Predicting major adverse cardiovascular events in angina patients using radiomic features of pericoronary adipose tissue based on CCTA.Frontiers in cardiovascular medicine · 2024Article
- Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: a narrative review.Rheumatology international · 2023Review
- Perivascular adipose tissue as a source of therapeutic targets and clinical biomarkers.European heart journal · 2023Review
- Deep-Learning for Epicardial Adipose Tissue Assessment With Computed Tomography: Implications for Cardiovascular Risk Prediction.JACC. Cardiovascular imaging · 2023Article
- From 'Omics to Multi-omics Technologies: the Discovery of Novel Causal Mediators.Current atherosclerosis reports · 2023Review
- Radiomic Phenotype of Periatrial Adipose Tissue in the Prognosis of Late Postablation Recurrence of Idiopathic Atrial Fibrillation.Sovremennye tekhnologii v meditsine · 2023Article
- Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging.PLOS digital health · 2023Article
- Artificial intelligence in coronary computed tomography angiography: Demands and solutions from a clinical perspective.Frontiers in cardiovascular medicine · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
abstractRapid technological advances in non-invasive imaging, coupled with the availability of large data sets and the expansion of computational models and power, have revolutionized the role of imaging in medicine. Non-invasive imaging is the pillar of modern cardiovascular diagnostics, with modalities such as cardiac computed tomography (CT) now recognized as first-line options for cardiovascular risk stratification and the assessment of stable or even unstable patients. To date, cardiovascular imaging has lagged behind other fields, such as oncology, in the clinical translational of artificial intelligence (AI)-based approaches. We hereby review the current status of AI in non-invasive cardiovascular imaging, using cardiac CT as a running example of how novel machine learning (ML)-based radiomic approaches can improve clinical care. The integration of ML, deep learning, and radiomic methods has revealed direct links between tissue imaging phenotyping and tissue biology, with important clinical implications. More specifically, we discuss the current evidence, strengths, limitations, and future directions for AI in cardiac imaging and CT, as well as lessons that can be learned from other areas. Finally, we propose a scientific framework in order to ensure the clinical and scientific validity of future studies in this novel, yet highly promising field. Still in its infancy, AI-based cardiovascular imaging has a lot to offer to both the patients and their doctors as it catalyzes the transition towards a more precise phenotyping of cardiovascular disease.
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What Socratic 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 Socratic graph.