ReviewInterventional cardiology (London, England)2021
Cardiovascular Imaging and Intervention Through the Lens of Artificial Intelligence.
Review in Interventional cardiology (London, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Intracoronary Imaging for the Management of Vulnerable Plaques.Journal of clinical medicine · 2026Review
- Proposing the ValvUS approach: integrating bedside tests and ultrasonography for severe valvular heart disease diagnosis.Einstein (Sao Paulo, Brazil) · 2026Review
- Automated Artificial Intelligence Mapping of Coronary Plaque Calcification: A Comparison with Manual Intravascular Image Analysis.Journal of clinical medicine · 2025Article
- Harnessing Artificial Intelligence in Interventional Cardiology: A Systematic Review of Current Applications.Cureus · 2025Review
- Broadening Perspectives of Artificial Intelligence in Echocardiography.Cardiology and therapy · 2024Review
- SPECT-MPI for Coronary Artery Disease: A Deep Learning Approach.Acta medica Philippina · 2024Article
- Advancements in artificial intelligence-driven techniques for interventional cardiology.Cardiology journal · 2024Review
- Use of Artificial Intelligence in the Classification of Elementary Oral Lesions from Clinical Images.International journal of environmental research and public health · 2023Article
- Imaging of Left Main Coronary Artery; Untangling the Gordian Knot.Reviews in cardiovascular medicine · 2023Review
- Applications of Machine Learning in Cardiology.Cardiology and therapy · 2022Review
- Big Data in Cardiology: State-of-Art and Future Prospects.Frontiers in cardiovascular medicine · 2022Review
- Artificial intelligence in cardiology: The past, present and future.Indian heart journalArticle
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
No grant is acknowledged in the PubMed record.
Abstract
Artificial Intelligence (AI) is the simulation of human intelligence in machines so they can perform various actions and execute decision-making. Machine learning (ML), a branch of AI, can analyse information from data and discover novel patterns. AI and ML are rapidly gaining prominence in healthcare as data become increasingly complex. These algorithms can enhance the role of cardiovascular imaging by automating many tasks or calculations, find new patterns or phenotypes in data and provide alternative diagnoses. In interventional cardiology, AI can assist in intraprocedural guidance, intravascular imaging and provide additional information to the operator. AI is slowly expanding its boundaries into interventional cardiology and can fundamentally alter the field. In this review, the authors discuss how AI can enhance the role of cardiovascular imaging and imaging in interventional cardiology.
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.