Machine-Learning-Assisted Lateral Flow Assay for COVID-19 and Influenza Detection

Abstract

In the pandemic and endemic era, lateral flow assay (LFA) is a great candidate for point-of-care testing, and especially as a tool for companion diagnostics. For example, fast detection and immediate treatment are key factors in companion diagnostics for oral antiviral treatments, such as the use of Paxlovid and Tamiflu for treating coronavirus disease and influenza, respectively. However, several limitations are exhibited when untrained individuals (i.e., during self-testing) analyze the results with the naked eye. Herein, we propose a method to achieve enhanced assay performance via the use of machine-learning-assisted smartphone image processing and machine-learning-assisted LFA readers. The use of machine-learning-assisted LFA readers demonstrated a seven-fold increase in the sensitivity slope when detecting influenza A virus with enhanced R² values (0.90). Moreover, when using smartphone-based digital images, we achieved an eight-fold enhancement in the sensitivity slope when detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with good reliability (R² values of ~0.96).

Note:
Funding Information: This work was supported by the Bio & Medical Technology Development Program of the National Research Foundation funded by the Korean government (MSIT) (No. 2021M3E5E3080743). J.H. Lee was supported by a research grant from Kwangwoon University in 2022.

Conflict of Interests: The authors declare that they have no known competing financial interests or personal relationships that may have influenced the work reported in this paper.

Keywords: Lateral Flow Assay, Machine Learning, COVID-19, Influenza, AI-assisted, Diagnosis