Advanced Ultrasound in Diagnosis and Therapy ›› 2023, Vol. 7 ›› Issue (2): 152-157.doi: 10.37015/AUDT.2023.230018

• Original Research • Previous Articles     Next Articles

Rapid Screening of Carotid Plaque in Cloud Handheld Ultrasound System Based on 5G and AI Technology

Wenjun Zhang, MDa, Mi Zhou, PhDa, Qingguo Meng, MDb, Lin Zhang, MSc,*(), Xin Liu, MSc, Paul Liu, PhDc, Dong Liu, PhDd,e   

  1. a Department of Ultrasonography, Wenjiang District People's Hostpital of Chengdu, Chengdu, Sichuan, China
    b Cardiovascular Ultrasound and Non-Invasive Cardiology Department, Sichuan provincial people’s hospital, Chengdu, Sichuan
    c Stork Healthcare, Chengdu, Sichuan, China
    d West China School of Medicine, West China Hospital, Sichuan University
    e College of Computer Science, Sichuan University
  • Received:2023-03-30 Revised:2023-04-21 Accepted:2023-04-22 Online:2023-06-30 Published:2023-04-27
  • Contact: Lin Zhang, MS,


Objective: To evaluate the real-time accuracy of cloud handheld ultrasound system using AI technology in screening carotid plaque.

Methods: 2627 ultrasound images of the carotid artery are collected using the cloud handheld system. Bounding boxes of carotid plaques are labeled by qualified sonographers, and the dataset is trained using a lightweight YOLOv3 model. An additional and separate 390 images are collected and tested using the evaluation metrics average recall (AR), average precision (AP), and frames per second (FPS) for quantifying classification performance and time consumption.

Results: We use a plaque grading definition with a thickness of 1.2-1.5 mm defined as small plaque, 1.5-3 mm as medium plaque, and more than 3 mm thick as large plaque. Our model achieves APIoU=0.50 with 96.5%, with APlarge is 79.9%, APmedium is 90.7%, APsmall is 93.5%; ARIoU=0.50 is 64.5%, where ARlarge is 60.6%, ARmedium is 58.3%, ARsmall is 70.8%, and FPS is 33.3.

Conclusion: We establish a framework for data set construction, model selection, training, and testing of carotid ultrasound images and verify the effectiveness of real-time AI technology in the automatic detection of carotid artery plaque.

Key words: Handheld ultrasound; Carotid plaque; YOLOv3; Artificial intelligence (AI)