Machine learning models for carotid artery plaque detection: A systematic review of ultrasound-based diagnostic performance
Pooya Eini, Peyman Eini, Homa Serpoush, Mohammad Rezayee, Jason Tremblay
Abstract
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.