Machine learning to support visual auditing of home-based lateral flow immunoassay self-test results for SARS-CoV-2 antibodies.

Background: Lateral flow immunoassays (LFIAs) are being used worldwide for COVID-19 mass testing and antibody prevalence studies. Relatively simple to use and low cost, these tests can be self-administered at home, but rely on subjective interpretation of a test line by eye, risking false positives and false negatives. Here, we report on the development of ALFA (Automated Lateral Flow Analysis) to improve reported sensitivity and specificity. Methods: Our computational pipeline uses machine learning, computer vision techniques and signal processing algorithms to analyse images of the Fortress LFIA SARS-CoV-2 antibody self-test, and subsequently classify results as invalid, IgG negative and IgG positive. A large image library of 595,339 participant-submitted test photographs was created as part of the REACT-2 community SARS-CoV-2 antibody prevalence study in England, UK. Alongside ALFA, we developed an analysis toolkit which could also detect device blood leakage issues. Results: Automated analysis showed substantial agreement with human experts (Cohen's kappa 0.90-0.97) and performed consistently better than study participants, particularly for weak positive IgG results. Specificity (98.7-99.4%) and sensitivity (90.1-97.1%) were high compared with visual interpretation by human experts (ranges due to the varying prevalence of weak positive IgG tests in datasets). Conclusions: Given the potential for LFIAs to be used at scale in the COVID-19 response (for both antibody and antigen testing), even a small improvement in the accuracy of the algorithms could impact the lives of millions of people by reducing the risk of false-positive and false-negative result read-outs by members of the public. Our findings support the use of machine learning-enabled automated reading of at-home antibody lateral flow tests as a tool for improved accuracy for population-level community surveillance.

How can biophotonics help dentistry to avoid or minimize cross infection by SARS-CoV-2?

Biophotonics is defined as the combination of biology and photonics (the physical science of the light). It is a general term for all techniques that deal with the interaction between biological tissues/cells and photons (light). Biophotonics offers a great variety of techniques that can facilitate the early detection of diseases and promote innovative theragnostic approaches. As the COVID-19 infection can be transmitted due to the face-to-face communication, droplets and aerosol inhalation and the exposure to saliva, blood, and other body fluids, as well as the handling of sharp instruments, dental practices are at increased risk of infection. In this paper, a literature review was performed to explore the application of Biophotonics approaches in Dentistry focusing on the COVID-19 pandemic and how they can contribute to avoid or minimize the risks of infection in a dental setting. For this, search-related papers were retrieved from PubMED, Scielo, Google Schoolar, and American Dental Association and Centers for Disease Control and Prevention databases. The body of evidence currently available showed that Biophotonics approaches can reduce microorganism load, decontaminate surfaces, air, tissues, and minimize the generation of aerosol and virus spreading by minimally invasive, time-saving, and alternative techniques in general. However, each clinical situation must be individually evaluated regarding the benefits and drawbacks of these approaches, but always pursuing less-invasive and less aerosol-generating procedures, especially during the COVID-19 pandemic.

Light-based technologies for management of COVID-19 pandemic crisis.

The global dissemination of the novel coronavirus disease (COVID-19) has accelerated the need for the implementation of effective antimicrobial strategies to target the causative agent SARS-CoV-2. Light-based technologies have a demonstrable broad range of activity over standard chemotherapeutic antimicrobials and conventional disinfectants, negligible emergence of resistance, and the capability to modulate the host immune response. This perspective article identifies the benefits, challenges, and pitfalls of repurposing light-based strategies to combat the emergence of COVID-19 pandemic.