Jing Min, Mclaughlin Donal, Mcnamee Sara E, Raj Shasidran, Namee Brian Mac, Steele David, Finlay Dewar, Mclaughlin James
School of EngineeringUlster University Jordanstown BT37 0QB U.K.
Department of Physics and AstronomyUniversity College London London WC1E 6BT U.K.
IEEE J Transl Eng Health Med. 2021 Nov 23;9:1900415. doi: 10.1109/JTEHM.2021.3130494. eCollection 2021.
To design and implement an easy-to-use, Point-of-Care (PoC) lateral flow immunoassays (LFA) reader and data analysis system, which provides a more in-depth quantitative analysis for LFA images than conventional approaches thereby supporting efficient decision making for potential early risk assessment of cardiovascular disease (CVD). A novel end-to-end system was developed including a portable device with CMOS camera integrated with optimized illumination and optics to capture the LFA images produced using high-sensitivity C-Reactive Protein (hsCRP) (concentration level < 5 mg/L). The images were transmitted via WiFi to a back-end server system for image analysis and classification. Unlike common image classification approaches which are based on averaging image intensity from a region-of-interest (ROI), a novel approach was developed which considered the signal along the sample's flow direction as a time series and, consequently, no need for ROI detection. Long Short-Term Memory (LSTM) networks were deployed for multilevel classification. The features based on Dynamic Time Warping (DTW) and histogram bin counts (HBC) were explored for classification. For the classification of hsCRP, the LSTM outperformed the traditional machine learning classifiers with or without DTW and HBC features performed the best (with mean accuracy of 94%) compared to other features. Application of the proposed method to human plasma also suggests that HBC features from LFA time series performed better than the mean from ROI and raw LFA data. As a proof of concept, the results demonstrate the capability of the proposed framework for quantitative analysis of LFA images and suggest the potential for early risk assessment of CVD. The hsCRP levels < 5 mg/L were aligned with clinically actionable categories for early risk assessment of CVD. The outcomes demonstrated the real-world applicability of the proposed system for quantitative analysis of LFA images, which is potentially useful for more LFA applications beyond presented in this study.
设计并实现一个易于使用的即时检测（PoC）侧向流动免疫分析（LFA）阅读器和数据分析系统，该系统能为LFA图像提供比传统方法更深入的定量分析，从而支持对心血管疾病（CVD）潜在早期风险评估进行高效决策。开发了一种新颖的端到端系统，包括一个集成了CMOS相机、优化照明和光学元件的便携式设备，用于捕获使用高灵敏度C反应蛋白（hsCRP）（浓度水平<5mg/L）产生的LFA图像。图像通过WiFi传输到后端服务器系统进行图像分析和分类。与基于感兴趣区域（ROI）平均图像强度的常见图像分类方法不同，开发了一种新颖的方法，该方法将沿样本流动方向的信号视为时间序列，因此无需进行ROI检测。部署了长短期记忆（LSTM）网络进行多级分类。探索了基于动态时间规整（DTW）和直方图箱计数（HBC）的特征进行分类。对于hsCRP的分类，LSTM优于传统机器学习分类器，与其他特征相比，具有DTW和HBC特征的分类器表现最佳（平均准确率为94%）。将所提出的方法应用于人体血浆也表明，来自LFA时间序列的HBC特征比ROI平均值和原始LFA数据表现更好。作为概念验证，结果证明了所提出框架对LFA图像进行定量分析的能力，并表明了CVD早期风险评估的潜力。hsCRP水平<5mg/L与CVD早期风险评估的临床可操作类别一致。结果证明了所提出系统对LFA图像进行定量分析的实际适用性，这对于本研究中未涉及的更多LFA应用可能具有潜在用途。
