ABSTRACT
There continues to be a great need for rapid, accurate, and cost-effective point-of-care devices that can diagnose the presence of SARS-CoV-2 virus and development of IgG and IgM antibody responses in early and late stages of COVID-19 disease. Here, we describe a versatile multiplexed electrochemical (EC) sensor platform modified with an antifouling nanocomposite coating that enables single-molecule CRISPR/Cas-based molecular detection of SARS-CoV-2 viral RNA with on-chip signal validation as well as multiplexed serological detection of antibodies against three SARS-CoV-2 viral antigens. The CRISPR-based EC platform achieved 100% accuracy for detection of viral RNA and showed an excellent correlation with RT-qPCR using 30 clinical saliva samples. The serology EC platform obtained 100% sensitivity and 100% specificity for anti-SARS-CoV-2 IgG, as well as 94% specificity and 82% sensitivity for anti-SARS-CoV-2 IgM with 112 clinical plasma samples. These data demonstrate that integration of CRISPR-based RNA detection and serological assays with antifouling nanocomposite-based EC sensors enables performance as good or better than traditional laboratory-based techniques.
Subject(s)
COVID-19 , Severe Acute Respiratory SyndromeABSTRACT
Background: We introduce a novel speech processing framework, the MIT CBMM Open Voice Brain Model (OVBM), combining implementations of the 4 modules of intelligence: The brain OS chunks and overlaps audio samples and transfers CNN features from the sensory stream and cognitive core creating a multi-modal graph neural network of symbolic compositional models for the target task.Methods: Our approach consists of pre-training models to extract acoustic features from selected biomarkers and then leverage transfer learning to combine the biomarker feature extractors into a graph neural network to provide an explainable diagnsotic for Alzheimer's Dementia (AD) using speech recordings.Results: We apply OVBM to the automated diagnostic of Alzheimer's Dementia patients, achieving above state-of-the-art accuracy of 93.8% using only raw audio, while extracting a personalized subject saliency map to track relative disease progression of 16 explainable biomarkers.Conclusion: By using independent biomarker models, OVBM lets health experts explore biomarker features and whether there are common biomarkers features between AD and other diseases like COVID-19. We present a novel lungs and respiratory tract biomarker created using 200.000+ cough samples to pre-train a model discriminating English from Catalan coughs. Transfer Learning is subsequently used to transfer features from this model with various other biomarker OVBM models. This strategy yielded consistent improvements in ADdetection, no matter the combination used. This cough dataset sets a new benchmark as largest audio health dataset with 30.000+ subjects participating in April 2020, demonstrating for the rst time cough cultural bias.
Subject(s)
Alzheimer Disease , Voice Disorders , COVID-19 , CoughABSTRACT
Just like your phone can detect what song is playing in crowded spaces, we show that Artificial Intelligence transfer learning algorithms trained on cough phone recordings results in diagnostic tests for COVID-19. To gain adoption by the health care community, we plan to validate our results in a clinical trial and three other venues in Mexico, Spain and the USA . However, if we had data from other on-going clinical trials and volunteers, we may do much more. For example, for confirmed stay-at-home COVID-19 patients, a longitudinal audio test could be developed to determine contact-with-hospital recommendations, and for the most critical COVID-19 patients a success ratio forecast test, including patient clinical data, to prioritize ICU allocation. As a challenge to the engineering community and in the context of our clinical trial, the authors suggest distributing cough recordings daily, hoping other trials and crowdsourcing users will contribute more data. Previous approaches to complex AI tasks have either used a static dataset or were private efforts led by large corporations. All existing COVID-19 trials published also follow this paradigm. Instead, we suggest a novel open collective approach to large-scale real-time health care AI. We will be posting updates at https://opensigma.mit.edu. Our personal view is that our approach is the right one for large scale pandemics, and therefore is here to stay - will you join?