Adaptable Automated Interpretation of Rapid Diagnostic Tests Using Few-Shot Learning

Point-of-care lateral flow assays (LFAs) are becomingly increasingly prevalent for diagnosing individual patient disease status and surveying population disease prevalence in a timely, scalable, and cost-effective manner, but a central challenge is to assure correct assay operation and results interpretation as the assays are manually performed in decentralized settings. A smartphone-based software can automate interpretation of an LFA kit, but such algorithms typically require a very large number of images of assays tested with validated specimens, which is challenging to collect for different assay kits, especially for those released during a pandemic. Here, we present an approach - AutoAdapt LFA - that uses few-shot learning, an approach used in other applications such as computer vision and robotics, for accurate and automated interpretation of LFA kits that requires a small number of validated images for training. The approach consists of three components: extraction of membrane and zone areas from an image of the LFA kit, a self-supervised encoder that employs a feature extractor trained with edge-filtered patterns, and few-shot adaptation that enables generalization to new kits using limited validated images. From a base model pre-trained on a commercial LFA kit, we demonstrated the ability of adapted models to interpret results from five new COVID-19 LFA kits (three detecting antigens for diagnosing active infection, and two detecting antibodies for diagnosing past infection). Specifically, using just 10 to 20 images of each new kit, we achieved accuracies of 99% to 100% for each kit. The server-hosted algorithm has an execution time of approximately 4 seconds, which can potentially enable quality assurance and linkage to care for users operating new LFAs in decentralized settings.