Development of a Decentralized Web App (D-App) for Covid-19 using AI

A horrifying number of people died because of the COVID-19 pandemic. There was an unexpected threat to food systems, public health, and the workplace. The pandemic has severely disturbed society and there was a serious impediment to the economy. The world went through an unprecedented state of chaos during this period. To avoid anything similar, we can only be cautious. The project aims to develop a web application for the preliminary detection of COVID-19 using Artificial Intelligence(AI). This project would enable faster coordination, secured data storage, and normalized statistics. First, the available chest X-ray datasets were collected and classified as Covid, Non-Covid, and Normal. Then they were trained using various state-of-the-art pre-trained Convolutional Neural Network (CNN) models with the help of Tensor-flow. Further, they were ranked based on their accuracy. The best-performing models were ensembled into a single model to improve the performance. The model with the highest accuracy was transformed into an application programming interface (API) and integrated with the Decentralized application (D-App). The user needs to upload an image of their chest X-ray, and the D-App then suggests if they should take a reverse transcription-polymerase chain reaction (RT-PCR) test for confirmation. © 2022 IEEE.

Digital detection and quantification of SARS-CoV-2 in a droplet microfluidic all-fiber device

Silica fibers and capillaries offer opportunities for compact integration of optics with microfluidics while adding advantages such as;flexibility within a high aspect ratio format, uniaxial arrangements, and measurement-at-a-distance. Here, we describe droplet microfluidics-based nucleic acid detection of SARS-CoV-2 in a lab-in-a-fiber platform. The fiber component integrates three modules with key functions: droplet generation, incubation, and fluorescence detection. Within the scope of this work, we developed the component specifically to target the quantification of SARS-CoV-2 viral RNA through reverse-transcription loop-mediated isothermal amplification (RT-LAMP). The all-fiber component could successfully generate uniform droplets and differentiate pre-amplified positive LAMP reaction from negative sample. © 2021 MicroTAS 2021 - 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences. All rights reserved.

Blending camera and 77 GHz radar sensing for equitable, robust plethysmography

With the resurgence of non-contact vital sign sensing due to the COVID-19 pandemic, remote heart-rate monitoring has gained significant prominence. Many existing methods use cameras;however previous work shows a performance loss for darker skin tones. In this paper, we show through light transport analysis that the camera modality is fundamentally biased against darker skin tones. We propose to reduce this bias through multi-modal fusion with a complementary and fairer modality - radar. Through a novel debiasing oriented fusion framework, we achieve performance gains over all tested baselines and achieve skin tone fairness improvements over the RGB modality. That is, the associated Pareto frontier between performance and fairness is improved when compared to the RGB modality. In addition, performance improvements are obtained over the radar-based method, with small trade-offs in fairness. We also open-source the largest multi-modal remote heart-rate estimation dataset of paired camera and radar measurements with a focus on skin tone representation. © 2022 Owner/Author.

Viral detection and quantification in a digital droplet microfluidic lab-in-a-fiber device

In this work, we present the design and fabrication of a fiber device that performs digital droplet microfluidics for molecular diagnostics. A variety of fibers and capillaries were used to build three connected modules dedicated to droplet generation, incubation, and fluorescence detection which enables a uniaxial arrangement. This is in contrast to the traditional 2-dimensional lab-on-a-chip architecture. We characterize our fiber device using a fluorescein dilution series. Our observed detection limit is on the order of 10 nM fluorescein. We demonstrate our all-fiber device for the fluorescence readout after loop-mediated isothermal amplification (LAMP) of synthetic SARS-CoV-2. Our results suggest that this fiber device can successfully distinguish between positive and negative samples in molecular diagnostics. We propose that our fiber device offers benefits over microfluidic chip techniques such as easier optical integration, much simpler sample loading, and faster diagnosis with high specificity and sensitivity. Keywords: All-fiber device, microfluidics, optofluidics, loop-mediated isothermal amplification (LAMP), molecular diagnostics, SARS-CoV2. © 2021 SPIE.