2019年冠状病毒病(COVID-19)
肺超声
肺炎
2019-20冠状病毒爆发
严重急性呼吸综合征冠状病毒2型(SARS-CoV-2)
医学
肺
代表(政治)
重症监护医学
病毒学
内科学
政治学
法学
传染病(医学专业)
疾病
爆发
政治
作者
Zhiqiang Li,Xueping Yang,Hengrong Lan,Mixue Wang,Lijie Huang,Xingyue Wei,Gangqiao Xie,Sheng Wang,Jing Yu,Qiong He,Yao Zhang,Jianwen Luo
出处
期刊:Ultrasonics
[Elsevier]
日期:2024-07-20
卷期号:143: 107409-107409
标识
DOI:10.1016/j.ultras.2024.107409
摘要
COVID-19 pneumonia severity assessment is of great clinical importance, and lung ultrasound (LUS) plays a crucial role in aiding the severity assessment of COVID-19 pneumonia due to its safety and portability. However, its reliance on qualitative and subjective observations by clinicians is a limitation. Moreover, LUS images often exhibit significant heterogeneity, emphasizing the need for more quantitative assessment methods. In this paper, we propose a knowledge fused latent representation framework tailored for COVID-19 pneumonia severity assessment using LUS examinations. The framework transforms the LUS examination into latent representation and extracts knowledge from regions labeled by clinicians to improve accuracy. To fuse the knowledge into the latent representation, we employ a knowledge fusion with latent representation (KFLR) model. This model significantly reduces errors compared to approaches that lack prior knowledge integration. Experimental results demonstrate the effectiveness of our method, achieving high accuracy of 96.4 % and 87.4 % for binary-level and four-level COVID-19 pneumonia severity assessments, respectively. It is worth noting that only a limited number of studies have reported accuracy for clinically valuable exam level assessments, and our method surpass existing methods in this context. These findings highlight the potential of the proposed framework for monitoring disease progression and patient stratification in COVID-19 pneumonia cases.
科研通智能强力驱动
Strongly Powered by AbleSci AI