ReviewDiagnostics (Basel, Switzerland)2021
Machine Learning Quantitation of Cardiovascular and Cerebrovascular Disease: A Systematic Review of Clinical Applications.
Review in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Hyperparameter selection for dataset-constrained semantic segmentation: Practical machine learning optimization.Journal of applied clinical medical physics · 2024Article
- Review
- RGX Ensemble Model for Advanced Prediction of Mortality Outcomes in Stroke Patients.BME frontiers · 2024Article
- Using artificial intelligence in the development of diagnostic models of coronary artery disease with imaging markers: A scoping review.Frontiers in cardiovascular medicine · 2022Article
- Artificial intelligence, machine learning, and deep learning for clinical outcome prediction.Emerging topics in life sciences · 2021Article
- Observer Variability in CT Angiography Carotid Segmentation: Assessing Variability to Set Minimum Clinical Performance.Journal of neuroimaging : official journal of the American Society of NeuroimagingArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Research into machine learning (ML) for clinical vascular analysis, such as those useful for stroke and coronary artery disease, varies greatly between imaging modalities and vascular regions. Limited accessibility to large diverse patient imaging datasets, as well as a lack of transparency in specific methods, are obstacles to further development. This paper reviews the current status of quantitative vascular ML, identifying advantages and disadvantages common to all imaging modalities. Literature from the past 8 years was systematically collected from MEDLINE
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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.