Machine learning models for carotid artery plaque detection: A systematic review of ultrasound-based diagnostic performance

Background

Carotid artery plaques, a hallmark of atherosclerosis, are key risk indicators for ischemic stroke, a major global health burden with 101 million cases and 6.65 million deaths in 2019. Early ultrasound detection is vital but hindered by manual analysis limitations. Machine learning (ML) offers a promising solution for automated plaque detection, yet its comparative performance is underexplored. This systematic review and meta-analysis evaluates ML models for carotid plaque detection using ultrasound.

Methods

We searched PubMed, Scopus, Embase, Web of Science, and ProQuest for studies on ML-based carotid plaque detection with ultrasound, following PRISMA guidelines. Eligible studies reported diagnostic metrics and used a reference standard. Data on study characteristics, ML models, and performance were extracted, with risk of bias assessed via PROBAST+AI. Pooled sensitivity, specificity, AUROC were calculated using STATA 18 with MIDAS and METADTA modules.

Results

Of ten studies, eight were meta-analyzed (200–19,751 patients) Best models showed a pooled sensitivity 0.94 (95% CI: 0.88–0.97), specificity 0.95 (95% CI: 0.86–0.98), AUROC 0.98 (95% CI: 0.97–0.99), and DOR 302 (95% CI: 54–1684), with high heterogeneity (I² = 90%) and no publication bias.

Conclusion

ML models show promise in carotid plaque detection, supporting potential clinical integration for stroke prevention, though high heterogeneity and potential bias highlight the need for standardized validation.