Smartphone-Powered Automated Image Recognition Tool for Multianalyte Rapid Tests: Application to Infectious Diseases

Point-of-Care Testing (POCT) is rapidly increasing, providing quick, user-friendly, and portable diagnostic tools. Lateral flow assays (LFAs) have been central to POCT, administering fast and cost-effective diagnosis. However, traditional LFAs are limited to qualitative or semiquantitative results. The integration of artificial intelligence (AI) and image analysis with LFAs has significantly improved diagnostic accuracy, result automation, and quantification where applicable. ΑΙ/image analysis algorithms are trained to automatically correlate the visual results with the presence of the analyte in the sample. Smartphone-based devices increase accessibility but also face challenges such as strip positioning and background lighting, which image analysis can potentially address. This study demonstrates a smartphone and machine vision-driven multicolor LFA, as well as an additional independent AI tool, for detecting pathogens like <i>E. coli</i> and SARS-CoV-2 in a single test. The developed system was successfully applied to real samples, providing accurate and multiplex results, advancing the field of infectious disease diagnostics. The results are presented as color, text, and audio messages, meeting all special needs of the users.