SynthesisBMC medical informatics and decision making2025
Artificial intelligence diagnostic performance in image-based vulnerable carotid plaque detection: a systematic review and meta-analysis.
Synthesis in BMC medical informatics and decision making, 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.
- MicroRNA Profiling of the Pathophysiological Mechanisms Underlying Carotid Atherosclerosis Progression.Biomolecules · 2026Review
- A radiomics model based on CTA subtraction imaging for differentiating symptomatic and asymptomatic carotid plaques: a retrospective study.BMC cardiovascular disorders · 2026Article
- Assessing the Prognostic Significance of Ultrasound - Detected Carotid Plaque Morphology in Predicting Ischemic Stroke Risk Among Postmenopausal Women.International journal of women's health · 2026Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAtherosclerosis in the carotid artery significantly contributes to embolic events leading to ischemic stroke. Precise identification of unstable carotid plaques through non-invasive imaging, is pivotal for stroke prevention. Artificial intelligence (AI) has demonstrated promise in enhancing the accuracy of plaque risk stratification. This review aims to assess the diagnostic performance of AI algorithms in distinguishing unstable carotid plaques from stable plaques using medical imaging.
methodsWe conducted comprehensive searches in Medline, Embase, Web of Science, IEEE, PubMed, and the Cochrane Library up to June 6, 2023. Eligible studies included those that utilized AI algorithms for identifying unstable carotid plaques from medical images. Binary diagnostic accuracy metrics, including sensitivity, specificity, and Area Under the Curve (AUC), were extracted. QUADAS-AI was used to assess risk of bias of the included studies.
resultsAmong the 31 studies reviewed, 14 were subjected to meta-analysis, revealing a pooled sensitivity of 91% (95%CI: 86 - 95%), specificity of 84% (79 - 89%), and AUC of 0.94 (0.91 - 0.95). However, only one study reported external validation, limiting the generalizability of these findings, and substantial heterogeneity was observed (I
conclusionsAI algorithms demonstrated favorable diagnostic performance in identifying unstable carotid plaques. Future research should focus on rigorous validation, ensuring generalizability, and enhancing the explainability of AI algorithms to facilitate their translational use. CLINICAL TRIAL NUMBER: Not applicable.
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