ReviewCurrent cardiology reports2020
Artificial Intelligence in Intracoronary Imaging.
Review in Current cardiology reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 52 citations in OpenAlex.
- The Application and Development of Deep Learning in Radiotherapy: A Systematic Review.Technology in cancer research & treatmentPooled it
- Machine learning integrated clinical-proteomics data identifies a 6-protein panel signature for atherosclerotic severity and enhanced patient stratification.Molecular biomedicine · 2026Article
- An Investigation Into the Comprehension, Perceptions, Application, and Determinants Affecting the Utilization of Deepseek Artificial Intelligence by Healthcare Professionals: A Cross-Sectional Study.Health science reports · 2025Article
- Review and recommendations for using artificial intelligence in intracoronary optical coherence tomography analysis.European heart journal. Digital health · 2025Review
- Deep learning model DeepNeo predicts neointimal tissue characterization using optical coherence tomography.Communications medicine · 2025Article
- Optical coherence tomography to guide percutaneous coronary intervention.EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology · 2024Review
- Innovations in Intracoronary Imaging: Present Clinical Practices and Future Outlooks.Journal of clinical medicine · 2024Review
- Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look.Journal of cardiovascular development and disease · 2023Review
- Evaluation of a Rabbit Model of Vascular Stent Healing: Application of Optical Coherence Tomography.Journal of cardiovascular translational research · 2023Article
- Accuracy of IVUS-Based Machine Learning Segmentation Assessment of Coronary Artery Dimensions and Balloon Sizing.JACC. Advances · 2023Article
- Morphologies and composition changes in nonculprit subclinical atherosclerosis in diabetic versus nondiabetic patients with acute coronary syndrome who underwent long-term statin therapy.Scientific reports · 2023Observational
- Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging.PLOS digital health · 2023Article
- Vulnerable Atherosclerotic Plaque: Is There a Molecular Signature?International journal of molecular sciences · 2022Review
- Rapid lipid-laden plaque identification in intravascular optical coherence tomography imaging based on time-series deep learning.Journal of biomedical optics · 2022Article
- Current State and Future Perspectives of Artificial Intelligence for Automated Coronary Angiography Imaging Analysis in Patients with Ischemic Heart Disease.Current cardiology reports · 2022Review
- Research Progress of Machine Learning and Deep Learning in Intelligent Diagnosis of the Coronary Atherosclerotic Heart Disease.Computational and mathematical methods in medicine · 2022Review
- Artificial intelligence to generate medical images: augmenting the cardiologist's visual clinical workflow.European heart journal. Digital health · 2021Article
- Deep Learning for Detecting Supraspinatus Calcific Tendinopathy on Ultrasound Images.Journal of medical ultrasoundArticle
- Artificial intelligence in hemovigilance: A narrative review on advancing blood safety and monitoring systems.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
purpose of reviewThis paper investigates present uses and future potential of artificial intelligence (AI) applied to intracoronary imaging technologies. RECENT
findingsAdvances in data analytics and digitized medical imaging have enabled clinical application of AI to improve patient outcomes and reduce costs through better diagnosis and enhanced workflow. Applications of AI to IVUS and IVOCT have produced improvements in image segmentation, plaque analysis, and stent evaluation. Machine learning algorithms are able to predict future coronary events through the use of imaging results, clinical evaluations, laboratory tests, and demographics. The application of AI to intracoronary imaging holds significant promise for improved understanding and treatment of coronary heart disease. Even in these early stages, AI has demonstrated the ability to improve the prediction of cardiac events. Large curated data sets and databases are needed to speed the development of AI and enable testing and comparison among algorithms.
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.