Deep Multi-Task Attention Network for Automated, Reproducible Quantification of Carotid Plaque Burden in Ultrasound...

Carotid atherosclerotic plaque burden is a well-established biomarker of cerebrovascular and cardiovascular risk, yet its quantitative assessment from ultrasound imaging remains highly operator-dependent and poorly standardized. This study proposes a deep multi-task attention-based learning framework for automated, reproducible quantification of carotid plaque area from routine B-mode ultrasound images. The model integrates a ResNet-50 backbone with convolutional block attention modules (CBAM) to jointly learn plaque presence and normalized plaque area within a unified architecture. A total of 1,100 expert-annotated carotid ultrasound images were used, with 200 images reserved for independent validation. On the validation set, the proposed approach achieved a mean absolute error (MAE) of 0.001324 (corresponding to 2.44 mm² clinically), a root mean square error (RMSE) of 0.001891, and a Pearson correlation coefficient of 0.712 between predicted and reference plaque areas. The model demonstrated 65.2% improvement in MAE over U-Net segmentation pipelines and 37.0% improvement over ResNet-50 regression-only approaches. Bland-Altman analysis revealed minimal bias (mean difference: 0.0003) with narrow limits of agreement (-0.0034 to 0.0039), while intraclass correlation coefficient (ICC) reached 0.85, indicating excellent measurement reliability. Clinical assessment showed that 85% of measurements fell within clinically acceptable error thresholds (<4.0 mm²), enabling detection of plaque progression exceeding the 5.0 mm² minimal detectable change with 95% confidence. These findings demonstrate that attention-guided deep multi-task learning enables accurate, reproducible quantification of carotid plaque burden, directly supporting the reliable detection of clinically significant plaque progression and advancing clinical translation through agreement-centered validation.