AI-Enhanced Super-Resolution Handheld Ultrasound for Carotid Plaque Detection in Community Screening.

Authors

Jie Lan, Shun Liu, Haoming Chen, Tingyu Zhang, Penghui Zeng, Jun Li, Linyuan Jin, Tao Tan, Meng Du, Zhiyi Chen

Published in

Annals of family medicine. Volume 24. Issue 5. Pages 437-446. Sep 21, 2026. Epub Sep 21, 2026.

Abstract

Ischemic stroke is a leading cause of disability and death, and early identification of carotid atherosclerotic plaques is critical for stroke prevention in primary care and community settings. We examined whether artificial intelligence (AI)-enhanced handheld ultrasound (HHUS) could improve carotid plaque detection and stenosis assessment in community-based primary care screening.
We developed a super-resolution reconstruction model (Hyper-CycleGAN) and validated it in a clinical cohort of 127 patients with 198 plaques. The validated model was then applied in a community screening setting with 117 participants and 153 plaques, using portable ultrasound as the reference standard. We evaluated agreement with the reference standard (Bland-Altman, intraclass correlation coefficient) and community diagnostic performance (detection rates, quadratic-weighted Cohen κ, confusion matrices, and standard metrics).
In community screening, AI-enhanced HHUS identified 94.8% of carotid plaques compared with 87.6% using standard HHUS. Agreement in stenosis grading with the reference standard improved, with weighted κ increasing from 0.492 to 0.836. Sensitivity for identifying ultrasound-defined vulnerable plaques increased from 47.4% to 63.2%, and specificity remained high.
AI-enhanced HHUS improved carotid plaque detection and stenosis assessment in community screening, supporting more accurate risk stratification and referral decisions in primary care. This approach could expand access to low-cost stroke prevention strategies in underserved communities.Abstract available in: يبرع (Arabic); (Chinese); Francais (French); Deutsch (German); हिन्दी (Hindi); Indonesian (Indonesian); (Japanese); Portugues (Portuguese); Español (Spanish).

PMID:
42767949
Bibliographic data and abstract were imported from PubMed on 22 Sep 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.