ArticleCardiovascular diagnosis and therapy2026
A comparative study of the state-of-the-art artificial intelligence models for automated plaque measurement in a carotid ultrasound Japanese cohort.
Article in Cardiovascular diagnosis and therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Automated carotid intima-media thickness (cIMT) measurement from B-mode ultrasound is important for cardiovascular risk stratification. Direct comparisons of deep learning architectures on the same cohort under controlled conditions remain limited. This study aimed to compare the accuracy and computational cost of three deep learning architectures with the commercial AtheroEdge™ 2.0 (AE 2.0) system for carotid ultrasound analysis. Methods: This study evaluated three deep learning architectures: carotid non-attention double-stage convolutional neural network (cNonAtt-dsCNN), carotid attention-based single-stage UNet (cAtt-ssUNet), and carotid attention-based double-stage UNet (cAtt-dsUNet) against the commercial AE 2.0 system on 400 carotid ultrasound images from 200 Japanese patients. All deep learning models were trained using identical preprocessing, augmentation, hyperparameters (Adam optimizer, learning rate =1e-4, batch =32), and 5-fold cross-validation. Evaluation metrics included Dice similarity coefficient (DSC), Jaccard index, lumen-intima error (LIe), media-adventitia error (MAe), intima-media thickness error (IMTe), and computational cost. Results: Under identical conditions, cAtt-dsUNet achieved the highest accuracy [DSC: 91.2%, Jaccard index: 85.5%, correlation coefficient (CC): 0.98, LIe: 0.031 mm, IMTe: 0.022 mm], reaching commercial-grade overlap and correlation but at ~2.5× longer training (20.2 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.
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