Fig. 1. Overall approach of a rapidly adaptable model for interpreting images of rapid tests that require few training images.

a Overall process of automatically interpreting images from a diverse set of LFAs that span analytical targets, number of test and control bands, and housing and form factor. From a raw image of a LFA, a smartphone can automatically and accurately interpret the result within seconds, using a pre-trained machine-learning model that has been adapted to a specific test kit requiring only 20 images of each new rapid test kit. The considerable reduction in training images can bypass the procurement of large numbers of different types of rapid test kits and expert labeling with thousands of validated specimens per test kit, which is challenging during a pandemic, while ensuring patient health and safety and enabling public health monitoring of results. b Images of a base LFA kit (EcoTest) for pre-training the model, and five new COVID-19 LFA kits (including both antigen and antibody tests) that are interpreted using a rapidly adapted model. c Actual images used for training of a base model, and for rapid model adaptation for a specific new LFA test kit.