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. 2022 Feb 25;12(3):589.
doi: 10.3390/diagnostics12030589.

Open-Source, Adaptable, All-in-One Smartphone-Based System for Quantitative Analysis of Point-of-Care Diagnostics

Weronika Schary  1   2 , Filip Paskali  1   2 , Simone Rentschler  1   2   3 , Christoph Ruppert  1   2   3 , Gabriel E Wagner  4 , Ivo Steinmetz  4 , Hans-Peter Deigner  1   2   5   6 , Matthias Kohl  1   2
Affiliations

Open-Source, Adaptable, All-in-One Smartphone-Based System for Quantitative Analysis of Point-of-Care Diagnostics

Weronika Schary et al. Diagnostics (Basel). .

Abstract

Point-of-care (POC) diagnostics, in particular lateral flow assays (LFA), represent a great opportunity for rapid, precise, low-cost and accessible diagnosis of disease. Especially with the ongoing coronavirus disease 2019 (COVID-19) pandemic, rapid point-of-care tests are becoming everyday tools for identification and prevention. Using smartphones as biosensors can enhance POC devices as portable, low-cost platforms for healthcare and medicine, food and environmental monitoring, improving diagnosis and documentation in remote, low-resource locations. We present an open-source, all-in-one smartphone-based system for quantitative analysis of LFAs. It consists of a 3D-printed photo box, a smartphone for image acquisition, and an R Shiny software package with modular, customizable analysis workflow for image editing, analysis, data extraction, calibration and quantification of the assays. This system is less expensive than commonly used hardware and software, so it could prove very beneficial for diagnostic testing in the context of pandemics, as well as in low-resource countries.

Keywords: R Shiny application; lateral flow assays; point-of-care diagnostics; quantitative image analysis; smartphone-based system.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
An overview of the content and workflow of our system. The different coloured frames indicate which modules are included in which application. The red border of modules represents optional starting points in the workflow.
Figure 2
Figure 2
Image acquisition and editing functionality in the cropping and segmentation Tab of the LFA App mobile analysis (left) and LFA App analysis (right).
Figure 3
Figure 3
Background correction with three example images; (a) a grayscale LFA image acquired via BioImager (left) and its background correction results (right), (b) a colour LFA image taken with a smartphone (left) and its background correction results (right) and (c) a multicolour LFA image acquired with our smartphone imager (left) and its background correction results (right).
Figure 4
Figure 4
Results of the calibration analysis with LFA App mobile analysis (left) and LFA App analysis (right).
See this image and copyright information in PMC

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