Real-time carotid plaque recognition from dynamic ultrasound videos based on artificial neural network

超声波 人工神经网络 人工智能 计算机科学 医学 模式识别(心理学) 计算机视觉 放射科
作者
Wei Yao,Bin Yang,Ling Wei,Jun Xue,Yi‐Cheng Zhu,Jianchu Li,Mingwei Qin,Shuyang Zhang,Qing Dai,Meng Yang
出处
期刊:Ultraschall in Der Medizin [Georg Thieme Verlag]
标识
DOI:10.1055/a-2180-8405
摘要

Abstract Purpose Carotid ultrasound allows noninvasive assessment of vascular anatomy and function with real-time display. Based on the transfer learning method, a series of research results have been obtained on the optimal image recognition and analysis of static images. However, for carotid plaque recognition, there are high requirements for self-developed algorithms in real-time ultrasound detection. This study aims to establish an automatic recognition system, Be Easy to Use (BETU), for the real-time and synchronous diagnosis of carotid plaque from ultrasound videos based on an artificial neural network. Materials and Methods 445 participants (mean age, 54.6±7.8 years; 227 men) were evaluated. Radiologists labeled a total of 3259 segmented ultrasound images from 445 videos with the diagnosis of carotid plaque, 2725 images were collected as a training dataset, and 554 images as a testing dataset. The automatic plaque recognition system BETU was established based on an artificial neural network, and remote application on a 5G environment was performed to test its diagnostic performance. Results The diagnostic accuracy of BETU (98.5%) was consistent with the radiologist’s (Kappa = 0.967, P < 0.001). Remote diagnostic feedback based on BETU-processed ultrasound videos could be obtained in 150ms across a distance of 1023 km between the ultrasound/BETU station and the consultation workstation. Conclusion Based on the good performance of BETU in real-time plaque recognition from ultrasound videos, 5G plus Artificial intelligence (AI)-assisted ultrasound real-time carotid plaque screening was achieved, and the diagnosis was made.
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