ArticleFrontiers in cardiovascular medicine2021
Histopathology-Based Deep-Learning Predicts Atherosclerotic Lesions in Intravascular Imaging.
Article in Frontiers in cardiovascular medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed, 13 citations in OpenAlex.
- UNICORN: a deep learning model for integrating multi-stain data in histopathology.NPJ digital medicine · 2026Article
- Quantitative cardiac MRI assessment of aortic regurgitation and reverse remodeling after surgery: a multi-center study.The international journal of cardiovascular imaging · 2026Observational
- Artificial Intelligence-Led Whole Coronary Artery OCT Analysis; Validation and Identification of Drug Efficacy and Higher-Risk Plaques.Circulation. Cardiovascular imaging · 2025Article
- Pre-trained Artificial Intelligence Models in the Prediction and Classification of Atherosclerotic Cardiovascular Disease.The Eurasian journal of medicine · 2025Article
- Review and recommendations for using artificial intelligence in intracoronary optical coherence tomography analysis.European heart journal. Digital health · 2025Review
- Examination of the performance of machine learning-based automated coronary plaque characterization by near-infrared spectroscopy-intravascular ultrasound and optical coherence tomography with histology.European heart journal. Digital health · 2025Article
- Deep learning model DeepNeo predicts neointimal tissue characterization using optical coherence tomography.Communications medicine · 2025Article
- Deep-learning-driven optical coherence tomography analysis for cardiovascular outcome prediction in patients with acute coronary syndrome.European heart journal. Digital health · 2024Article
- Roadmap on the use of artificial intelligence for imaging of vulnerable atherosclerotic plaque in coronary arteries.Nature reviews. Cardiology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.