Can ultrasound image models help assess artery plaques?
Ultrasound models showed promise for carotid artery plaques labelled culprit and for plaque vulnerability assessment. The abstract does not explain these target labels or how they were checked; study quality was low.
Based on the published abstract. The full paper may contain additional methods, results and limitations.
The 30-second takeaway
The review examined ultrasound images of carotid artery plaques. Models showed promise for identifying plaques labelled culprit and assessing plaque vulnerability, but the abstract does not explain those target labels or how they were checked. Low study quality and varying results limit confidence. The abstract does not show improved patient outcomes.
Overall study quality was low, and results varied substantially between studies. The abstract does not explain how culprit plaques were confirmed or define plaque vulnerability, limiting assessment of what the reported diagnostic performance means in practice.
What the researchers assessed
Models combined quantitative ultrasound image analysis and machine learning to assess carotid artery plaques. The review used the target terms culprit plaques and plaque vulnerability, without defining them further in the abstract.
What diagnostic performance establishes
The reported results concern how models classified these plaques. They do not establish whether people benefited from using the models. The authors call for prospective research to confirm the techniques’ potential.
The original publication
Radiomics-based machine learning atherosclerotic carotid artery disease in ultrasound: systematic review with meta-analysis of RQS.
Vacca S, Scicolone R, Pisu F et al.
J Ultrasound · 2025
- PubMed ID
- 40489053
- Record checked
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