A comparative study of the state-of-the-art artificial intelligence models for automated plaque measurement in a carotid ultrasound Japanese cohort

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06 Nov 2026 0 comments

CONCLUSIONS: A clear trade-off between accuracy-efficiency exists across the evaluated methods. cAtt-dsUNet is optimal for high-accuracy research settings with adequate GPU resources, while cNonAtt-dsCNN and AE 2.0 are preferable for speed-critical clinical screening. The single-cohort Japanese dataset limits generalizability; multi-center, multi-ethnic validation is recommended before clinical deployment.