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

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, two antibody) using just 20 labeled images. RESULTS: Here we show AutoAdapt POC to yield 99% to 100% accuracy over 726 tests (350 positive, 376 negative). In a COVID-19 drive-through study with 74 untrained users self-testing, 98% found image collection easy, and the rapidly adapted models achieved classification accuracies of 100% on both COVID-19 antigen and antibody test kits. Compared with traditional visual interpretation on 105 test kit results, the algorithm correctly identified 100% of images; without a false negative as interpreted by experts. Finally, compared to a traditional convolutional neural network trained on an HIV test kit, the algorithm showed high accuracy while requiring only 1/50th of the training images. CONCLUSIONS: The study demonstrates how rapid domain adaptation in machine learning can provide quality assurance, linkage to care, and public health tracking for untrained users across diverse POC diagnostic tests.

It can be difficult to correctly interpret the results of rapid diagnostic tests that give a visual readout, such as COVID rapid tests. We developed a computational algorithm to interpret rapid test results using an image taken by a smartphone camera. This algorithm can easily be adapted for use on results from different test kits. The algorithm was accurate at interpreting results obtained by members of the public using various COVID rapid tests and diagnostic tests with similar outputs used for other infections. The use of this algorithm should enable accurate interpretation of rapid diagnostic tests by members of the public and hence enable improved medical care.

Biosensors for Personal Mobile Health: A System Architecture Perspective.

Advances in mobile biosensors, integrating developments in materials science and instrumentation, are fueling an expansion in health data being collected and analyzed in decentralized settings. For example, semiconductor-based sensors are enabling measurement of vital signs, and microfluidic-based sensors are enabling measurement of biochemical markers. As biosensors for mobile health are becoming increasingly paired with smart devices, it will become critical for researchers to design biosensors - with appropriate functionalities and specifications - to work seamlessly with accompanying connected hardware and software. This article describes recent research in biosensors, as well as current mobile health devices in use, as classified into four distinct system architectures that take into account the biosensing and data processing functions required in personal mobile health devices. We also discuss the path forward for integrating biosensors into smartphone-based mobile health devices.

Hydrogel Microfilaments toward Intradermal Health Monitoring.

Digital health promises a paradigm shift for medicine where biomarkers in individuals are continuously monitored to improve diagnosis and treatment of disease. To that end, a technology for minimally invasive quantification of endogenous analytes in bodily fluids will be required. Here, we describe a strategy for designing and fabricating hydrogel microfilaments that can penetrate the skin while allowing for optical fluorescence sensing. The polyacrylamide formulation was selected to provide high elastic modulus in the dehydrated state and optical transparency in the hydrated state. The microfilaments can be covalently tethered to a fluorescent aptamer to enable functional sensing. The microfilament array can penetrate the skin with low pain and without breaking, contact the dermal interstitial fluid, and be easily removed from the skin. In the future, hydrogel microfilaments could be integrated with a wearable fluorometer to serve as a platform for continuous, minimally invasive monitoring of intradermal biomarkers.

Designing hollow nano gold golf balls.

Hollow/porous nanoparticles, including nanocarriers, nanoshells, and mesoporous materials have applications in catalysis, photonics, biosensing, and delivery of theranostic agents. Using a hierarchical template synthesis scheme, we have synthesized a nanocarrier mimicking a golf ball, consisting of (i) solid silica core with a pitted gold surface and (ii) a hollow/porous gold shell without silica. The template consisted of 100 nm polystyrene beads attached to a larger silica core. Selective gold plating of the core followed by removal of the polystyrene beads produced a golf ball-like nanostructure with 100 nm pits. Dissolution of the silica core produced a hollow/porous golf ball-like nanostructure.