Development of a Universal Lateral-Flow Immunoassay Interpretation Device Prototype With Quantitative Output for Blind or Visually Impaired Individuals

The lateral-flow immunoassay (LFA) has proven to be an effective point-of-care (POC) diagnostic for the rapid detection of target analytes in patient samples. However, due to its reliance on a binary, visual output, the LFA cannot be used to quantify target analyte concentrations, nor is it accessible to blind or visually impaired patients. To address these limitations, we developed the quantitative LFA interpretation device (qLFAID), which operates on the Raspberry Pi 4 Model B. The qLFAID uses a trained neural network in conjunction with a nonlinear regression model to analyze the test line intensity of any LFA to determine the target analyte concentration. In addition, qLFAID’s electronic components provide auditory and tactile signals for blind and visually impaired users to interpret their results. As a proof of concept, the device was applied to analyze an LFA we designed to detect for digoxin in human serum. Our device successfully correlated the LFA test line intensity to the sample digoxin concentration, providing a quantitative output. Overall, qLFAID has the potential to be robust in POC settings and, to our knowledge, is the first assistive technology that can both enable the quantitative capability of all LFAs and increase diagnostic accessibility for blind or visually impaired individuals.