Rapidly adaptable automated interpretation of point-of-care COVID-19 diagnostics.

ABSTRACT:

Background

Point-of-care diagnostic devices, such as lateral-flow assays, are becoming widely used by the public. However, efforts to ensure correct assay operation and result interpretation rely on hardware that cannot be easily scaled or image processing approaches requiring large training datasets, necessitating large numbers of tests and expert labeling with validated specimens for every new test kit format.

Methods

We developed a software architecture called AutoAdapt POC that integrates automated membrane extraction, self-supervised learning, and few-shot learning to automate the interpretation of POC diagnostic tests using smartphone cameras in a scalable manner. A base model pre-trained on a single LFA kit is adapted to five different COVID-19 tests (three antigen, t

SUBMITTER: Arumugam S 

PROVIDER: S-EPMC10290128 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature