Abstract：
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.
摘要：
COVID-19肺炎严重程度评估具有重要的临床意义，由于其安全性和便携性，肺部超声（LUS）在帮助COVID-19肺炎的严重程度评估中起着至关重要的作用。然而,它依赖临床医生的定性和主观观察是一个限制。此外，LUS图像通常表现出显著的异质性，强调需要更多的定量评估方法。在本文中,我们提出了一个知识融合的潜在表示框架,用于使用LUS检查进行COVID-19肺炎严重程度评估.该框架将LUS检查转换为潜在表示，并从临床医生标记的区域中提取知识，以提高准确性。为了将知识融合到潜在的表现中，我们采用了具有潜在表示的知识融合(KFLR)模型。与缺乏先验知识集成的方法相比，此模型显着减少了错误。实验结果证明了该方法的有效性,二元水平和四级COVID-19肺炎严重程度评估的准确率分别为96.4%和87.4%，分别。值得注意的是，只有有限数量的研究报告了临床有价值的考试水平评估的准确性，在这种情况下，我们的方法超越了现有的方法。这些发现凸显了拟议框架在COVID-19肺炎病例中监测疾病进展和患者分层的潜力。
