SynthesisJournal of ultrasound2025
Radiomics-based machine learning atherosclerotic carotid artery disease in ultrasound: systematic review with meta-analysis of RQS.
Synthesis in Journal of ultrasound, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed.
- Beyond stenosis: Can artificial intelligence redefine stroke risk assessment in contrast-enhanced carotid ultrasound?European radiology · 2026Article
- Multidimensional clustering of clinical, metabolic, and ultrasound features for 30-day mortality risk stratification in ischemic stroke patients with carotid atherosclerotic plaque.Frontiers in neurology · 2026Article
- Application of Artificial Intelligence in Vulnerable Carotid Atherosclerotic Plaque Assessment-A Scoping Review.Medicina (Kaunas, Lithuania) · 2025Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundStroke, a leading global cause of mortality and neurological disability, is often associated with atherosclerotic carotid artery disease. Distinguishing between symptomatic and asymptomatic carotid artery disease is crucial for appropriate treatment decisions. Radiomics, a quantitative image analysis technique, and machine learning (ML) have emerged as promising tools in Ultrasound (US) imaging, potentially providing a helpful tool in the screening of such lesions.
methodsPubmed, Web of Science and Scopus databases were searched for relevant studies published from January 2005 to May 2023. The Radiomics Quality Score (RQS) was used to assess methodological quality of studies included in the review. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) assessed the risk of bias. Sensitivity, specificity, and logarithmic diagnostic odds ratio (logDOR) meta-analyses have been conducted, alongside an influence analysis.
resultsRQS assessed methodological quality, revealing an overall low score and consistent findings with other radiology domains. QUADAS-2 indicated an overall low risk, except for two studies with high bias. The meta-analysis demonstrated that radiomics-based ML models for predicting culprit plaques on US had a satisfactory performance, with a sensitivity of 0.84 and specificity of 0.82. The logDOR analysis confirmed the positive results, yielding a pooled logDOR of 3.54. The summary ROC curve provided an AUC of 0.887.
conclusionRadiomics combined with ML provide high sensitivity and low false positive rate for carotid plaque vulnerability assessment on US. However, current evidence is not definitive, given the low overall study quality and high inter-study heterogeneity. High quality, prospective studies are needed to confirm the potential of these promising techniques.
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