Now showing 1 - 2 of 2
  • Publication
    A Novel Method for Quantitative Analysis of C-Reactive Protein Lateral Flow Immunoassays Images via CMOS Sensor and Recurrent Neural Networks
    Objective: 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). Methods and procedures: 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. Results: 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. Conclusion: 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. Clinical impact: 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.
      8Scopus© Citations 5
  • Publication
    Enhance Categorisation Of Multilevel High-Sensitivity Cardiovascular Biomarkers From Lateral Flow Immunoassay Images Via Neural Networks And Dynamic Time Warping
    Lateral Flow Immunoassays (LFA) are low cost, rapid and highly efficacious Point-of-Care devices. Traditional LFA testing faces challenges to detect high-sensitivity biomarkers due to low sensitivity. Unlike most approaches based on averaging image intensity from a region-of-interest (ROI), this paper presents a novel system that considers each row of an LFA image as a time series signal and, consequently, does not require the detection of ROI. Long Short-Term Memory (LSTM) networks are used to classify LFA data obtained from multilevel high-sensitivity cardiovascular biomarkers. Dynamic Time Warping (DTW) was incorporated with LSTM to align the LFA data from different concentration levels to a common reference before feeding the distance maps into an LSTM network. The LSTM network outperforms other classifiers with or without DTW. Furthermore, performance of all classifiers is improved after incorporating DTW. The positive outcomes suggest the potential of the proposed methods for early risk assessment of cardiovascular diseases.
      30Scopus© Citations 5