Detection and Categorisation of Multilevel High-sensitivity Cardiovascular Biomarkers from Lateral Flow Immunoassay Images via Recurrent Neural Networks

Abstract

Lateral Flow Immunoassays (LFA) have the potential to provide low cost, rapid and highly efficacious Pointof-Care (PoC) diagnostic testing in resource limited settings. Traditional LFA testing is semi-quantitative based on the calibration curve, which faces challenges in the detection of multilevel high-sensitivity biomarkers due its low sensitivity. This paper proposes a novel framework in which the LFA images are acquired from a designed CMOS reader system under controlled lighting. Unlike most existing approaches based on image intensity, the proposed system does not require detection of region of interest (ROI), instead each row of the LFA image was considered as time series signals. The Long Short-Term Memory (LSTM) network was deployed to classify the LFA data obtained from cardiovascular biomarker, C-Reactive Protein (CRP), at eight concentration levels (within the range 0-5mg/L) that are aligned with clinically actionable categories. The performance under different arrangements for input dimension and parameters were evaluated. The preliminary results show that the proposed LSTM outperforms other popular classification methods, which demonstrate the capability of the proposed system to detect high-sensitivity CRP and suggests the potential of applications for early risk assessment of cardiovascular diseases (CVD).

A Novel Method for Quantitative Analysis of C-Reactive Protein Lateral Flow Immunoassays Images via CMOS Sensor and Recurrent Neural Networks

“…This study developed a novel approach considering the LFA data along the sample’s flow direction as time series signals (hence no need for ROI detection), which provided a new perspective to analyse the LFA image data and explore richer information than image intensity. For classification, the LSTM networks can be directly applied to the LFA time series signals (as in our pilot study [31] ) however the performance can be improved with additional features. Here, the temporal features were constructed from DTW [27] and histogram bin count.…”