Machine learning-based classification of carotid plaques via ultrasound: a systematic review and meta-analysis of diagnostic performance.
Authors
Affiliations (4)
Affiliations (4)
- Cardiovascular Research Center, Rajaie Cardiovascular Institute, Shaheed Rajaei Cardiovascular, Medical and Research Center, Vali-Asr St., Vanak, Tehran, Iran. [email protected].
- Infectious Disease Research Center, Hamadan University of Medical Sciences, Hamadan, Iran.
- Hamadan University of Medical Sciences, Hamadan, Iran.
- College of Human Medicine, Michigan State University, East Lansing, Michigan, USA.
Abstract
Machine learning (ML) models have gained traction for classifying carotid artery plaques via ultrasound imaging to differentiate high-risk (unstable) from low-risk (stable) plaques, a critical step for stroke risk prediction and guiding clinical interventions such as endarterectomy. However, prior studies report inconsistent diagnostic performance attributed to variations in algorithms, cohort diversity, and imaging protocols. This systematic review and meta-analysis aim to evaluate the pooled diagnostic accuracy of ML models for carotid plaque classification, addressing these inconsistencies to inform standardized clinical applications. Five electronic databases were systematically searched up to February 28, 2025, for studies reporting diagnostic performance metrics of ML-based models in carotid plaque classification. Pooled performance metrics were analyzed using STATA, and the risk of bias was assessed using the PROBAST+AI tool. A total of 20 studies met the inclusion criteria, of which 13 provided sufficient data for quantitative synthesis. Sample sizes ranged from 15 to 413 patients, with 115– 81,000 images per study. Mean ages ranged from 27.5 to 75 years, mostly 60–70, and male representation ranged from 47% to 81%, except for one all-female cohort. The pooled sensitivity was 0.84 (95% CI: 0.74–0.90) and specificity was 0.96 (95% CI: 0.89–0.98), with a pooled AUC of 0.95 (95% CI: 0.93–0.97). Substantial heterogeneity was observed (I<sup>2</sup> = 88.8% for sensitivity, 64.1% for specificity, and 68.1% overall). Meta-regression identified sample size and model architecture as significant sources of between-study heterogeneity. No evidence of publication bias was detected (<i>p</i> = 0.36). Quality assessment using PROBAST+AI indicated a low overall risk of bias in 70% of studies, moderate in 20%, and high in 10%. The GRADE approach rated the certainty of evidence as moderate, primarily due to inconsistency and study-level bias. Machine learning models demonstrate promising diagnostic accuracy for carotid plaque classification, showing high pooled sensitivity and specificity. However, substantial heterogeneity and only moderate certainty of evidence suggest that these findings should be interpreted with caution. The online version contains supplementary material available at 10.1186/s12245-025-01065-1.