Septic Arthritis Modeling Using Sonographic Fusion with Attention and Selective Transformation: a Preliminary Study

IF 2.9 2区 工程技术 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Journal of Digital Imaging Pub Date : 2024-09-16 DOI:10.1007/s10278-024-01259-8
Chung-Ming Lo, Kuo-Lung Lai
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Abstract

Conventionally diagnosing septic arthritis relies on detecting the causal pathogens in samples of synovial fluid, synovium, or blood. However, isolating these pathogens through cultures takes several days, thus delaying both diagnosis and treatment. Establishing a quantitative classification model from ultrasound images for rapid septic arthritis diagnosis is mandatory. For the study, a database composed of 342 images of non-septic arthritis and 168 images of septic arthritis produced by grayscale (GS) and power Doppler (PD) ultrasound was constructed. In the proposed architecture of fusion with attention and selective transformation (FAST), both groups of images were combined in a vision transformer (ViT) with the convolutional block attention module, which incorporates spatial, modality, and channel features. Fivefold cross-validation was applied to evaluate the generalized ability. The FAST architecture achieved the accuracy, sensitivity, specificity, and area under the curve (AUC) of 86.33%, 80.66%, 90.25%, and 0.92, respectively. These performances were higher than using conventional ViT (82.14%) and significantly better than using one modality alone (GS 73.88%, PD 72.02%), with the p-value being less than 0.01. Through the integration of multi-modality and the extraction of multiple channel features, the established model provided promising accuracy and AUC in septic arthritis classification. The end-to-end learning of ultrasound features can provide both rapid and objective assessment suggestions for future clinic use.

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利用声像图融合注意和选择性变换建立化脓性关节炎模型:初步研究
化脓性关节炎的传统诊断方法是在滑液、滑膜或血液样本中检测致病病原体。然而,通过培养分离这些病原体需要数天时间,从而延误了诊断和治疗。因此,必须从超声图像中建立一个定量分类模型,以快速诊断化脓性关节炎。为进行这项研究,我们建立了一个数据库,其中包括由灰度(GS)和功率多普勒(PD)超声波生成的 342 幅非化脓性关节炎图像和 168 幅化脓性关节炎图像。在所提出的注意力和选择性变换融合(FAST)架构中,两组图像都在视觉变换器(ViT)中与卷积块注意力模块相结合,后者结合了空间、模式和通道特征。五重交叉验证用于评估泛化能力。FAST 架构的准确率、灵敏度、特异性和曲线下面积(AUC)分别达到了 86.33%、80.66%、90.25% 和 0.92。这些性能均高于传统 ViT(82.14%),明显优于单独使用一种模式(GS 73.88%,PD 72.02%),P 值小于 0.01。通过多模态整合和多通道特征提取,所建立的模型在脓毒性关节炎分类中提供了良好的准确性和AUC。对超声特征的端到端学习可为今后的临床应用提供快速、客观的评估建议。
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来源期刊
Journal of Digital Imaging
Journal of Digital Imaging 医学-核医学
CiteScore
7.50
自引率
6.80%
发文量
192
审稿时长
6-12 weeks
期刊介绍: The Journal of Digital Imaging (JDI) is the official peer-reviewed journal of the Society for Imaging Informatics in Medicine (SIIM). JDI’s goal is to enhance the exchange of knowledge encompassed by the general topic of Imaging Informatics in Medicine such as research and practice in clinical, engineering, and information technologies and techniques in all medical imaging environments. JDI topics are of interest to researchers, developers, educators, physicians, and imaging informatics professionals. Suggested Topics PACS and component systems; imaging informatics for the enterprise; image-enabled electronic medical records; RIS and HIS; digital image acquisition; image processing; image data compression; 3D, visualization, and multimedia; speech recognition; computer-aided diagnosis; facilities design; imaging vocabularies and ontologies; Transforming the Radiological Interpretation Process (TRIP™); DICOM and other standards; workflow and process modeling and simulation; quality assurance; archive integrity and security; teleradiology; digital mammography; and radiological informatics education.
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