SARS-CoV-2 Result Interpretation based on Image Analysis of Lateral Flow Devices
This addresses the problem of result interpretation for health workers and users in COVID-19 testing, though it appears incremental as it applies existing ML methods to a new application.
The paper tackles the challenge of interpreting SARS-CoV-2 lateral flow device (LFD) results by developing an automated image analysis system that classifies images as positive, negative, or inconclusive, aiming to reduce human burden and bias.
The widely used gene quantisation technique, Lateral Flow Device (LFD), is now commonly used to detect the presence of SARS-CoV-2. It is enabling the control and prevention of the spread of the virus. Depending on the viral load, LFD have different sensitivity and self-test for normal user present additional challenge to interpret the result. With the evolution of machine learning algorithms, image processing and analysis has seen unprecedented growth. In this interdisciplinary study, we employ novel image analysis methods of computer vision and machine learning field to study visual features of the control region of LFD. Here, we automatically derive results for any image containing LFD into positive, negative or inconclusive. This will reduce the burden of human involvement of health workers and perception bias.