Goal: We hypothesized that COVID-19 subjects, especially including asymptomatics, could be accurately discriminated only from a forced-cough cell phone recording using Artificial Intelligence. To train our MIT Open Voice model we built a data collection pipeline of COVID-19 cough recordings through our website (opensigma.mit.edu) between April and May 2020 and created the largest audio COVID-19 cough balanced dataset reported to date with 5,320 subjects. Methods: We developed an AI speech processing framework that leverages acoustic biomarker feature extractors to pre-screen for COVID-19 from cough recordings, and provide a personalized patient saliency map to longitudinally monitor patients in real-time, non-invasively, and at essentially zero variable cost. Cough recordings are transformed with Mel Frequency Cepstral Coefficient and inputted into a Convolutional Neural Network (CNN) based architecture made up of one Poisson biomarker layer and 3 pre-trained ResNet50's in parallel, outputting a binary pre-screening diagnostic. Our CNN-based models have been trained on 4256 subjects and tested on the remaining 1064 subjects of our dataset. Transfer learning was used to learn biomarker features on larger datasets, previously successfully tested in our Lab on Alzheimer's, which significantly improves the COVID-19 discrimination accuracy of our architecture. Results: When validated with subjects diagnosed using an official test, the model achieves COVID-19 sensitivity of 98.5% with a specificity of 94.2% (AUC: 0.97). For asymptomatic subjects it achieves sensitivity of 100% with a specificity of 83.2% . Conclusions: AI techniques can produce a free, non-invasive, real-time, any-time, instantly distributable, large-scale COVID-19 asymptomatic screening tool to augment current approaches in containing the spread of COVID-19. Practical use cases could be for daily screening of students, workers, and public as schools, jobs, and transport reopen, or for pool testing to quickly alert of outbreaks in groups. General speech biomarkers may exist that cover several disease categories, as we demonstrated using the same ones for COVID-19 and Alzheimer's.

/pdf/covid-19-artificial-intelligence-diagnosis-using-only-cough-4487y32et6.pdf

COVID-19 Artificial Intelligence Diagnosis Using Only Cough Recordings

COVID-19 cough classification using machine learning and global smartphone recordings.

The leakage of medical audio data in telemedicine seriously violates the privacy of patients. In order to avoid the leakage of patient information in telemedicine, a two-stage reversible robust audio watermarking algorithm is proposed to protect medical audio data. The scheme decomposes the medical audio into two independent embedding domains, embeds the robust watermark and the reversible watermark into the two domains respectively. In order to ensure the audio quality, the Hurst exponent is used to find a suitable position for watermark embedding. Due to the independence of the two embedding domains, the embedding of the second-stage reversible watermark will not affect the first-stage watermark, so the robustness of the first-stage watermark can be well maintained. In the second stage, the correlation between the sampling points in the medical audio is used to modify the hidden bits of the histogram to reduce the modification of the medical audio and reduce the distortion caused by reversible embedding. Simulation experiments show that this scheme has strong robustness against signal processing operations such as MP3 compression of 48 db, additive white Gaussian noise (AWGN) of 20 db, low-pass filtering, resampling, re-quantization and other attacks, and has good imperceptibility. 

https://www.techscience.com/cmc/v71n2/45815/pdf

Robust Reversible Audio Watermarking Scheme for Telemedicine and Privacy Protection

Wearable devices provide an alternative pathway to clinical diagnostics by exploiting various physical, chemical and biological sensors to mine physiological (biophysical and/or biochemical) information in real time (preferably, continuously) and in a non-invasive or minimally invasive manner. These sensors can be worn in the form of glasses, jewellery, face masks, wristwatches, fitness bands, tattoo-like devices, bandages or other patches, and textiles. Wearables such as smartwatches have already proved their capability for the early detection and monitoring of the progression and treatment of various diseases, such as COVID-19 and Parkinson disease, through biophysical signals. Next-generation wearable sensors that enable the multimodal and/or multiplexed measurement of physical parameters and biochemical markers in real time and continuously could be a transformative technology for diagnostics, allowing for high-resolution and time-resolved historical recording of the health status of an individual. In this Review, we examine the building blocks of such wearable sensors, including the substrate materials, sensing mechanisms, power modules and decision-making units, by reflecting on the recent developments in the materials, engineering and data science of these components. Finally, we synthesize current trends in the field to provide predictions for the future trajectory of wearable sensors. 

/pdf/end-to-end-design-of-wearable-sensors-1qoptprj.pdf

End-to-end design of wearable sensors

A Survey on Deep Transfer Learning to Edge Computing for Mitigating the COVID-19 Pandemic

We developed an automated approach for generating quantitative image analysis metrics (imaging biomarkers) that are then analysed with a set of 13 machine learning algorithms to generate an overall risk score that is called a Q-score These methods were applied to a set of 120 "difficult" dermoscopy images of dysplastic nevi and melanomas that were subsequently excised/classified This approach yielded 98% sensitivity and 36% specificity for melanoma detection, approaching sensitivity/specificity of expert lesion evaluation Importantly, we found strong spectral dependence of many imaging biomarkers in blue or red colour channels, suggesting the need to optimize spectral evaluation of pigmented lesions

https://onlinelibrary.wiley.com/doi/pdf/10.1111/exd.13250

Digital imaging biomarkers feed machine learning for melanoma screening.

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 this https URL. Our personal view is that our approach is the right one for large scale pandemics, and therefore is here to stay - will you join?

Hi Sigma, do I have the Coronavirus?: Call for a New Artificial Intelligence Approach to Support Health Care Professionals Dealing With The COVID-19 Pandemic.

This RFC white Paper summarizes our progress on the MIT Voice Name System (VNS) and Huey. The VNS, similar in name and function to the DNS, is a system to reserve and use"wake words"to activate Artificial Intelligence (AI) devices. Just like you can say"Hey Siri"to activate Apple's personal assistant, we propose using the VNS in smart speakers and other devices to route wake requests based on commands such as"turn off","open grocery shopping list"or"271, start flash card review of my computer vision class". We also introduce Huey, an unambiguous Natural Language to interact with AI devices. We aim to standardize voice interactions to a universal reach similar to that of other systems such as phone numbering, with an agreed world-wide approach to assign and use numbers, or the Internet's DNS, with a standard naming system, that has helped flourish popular services including the World-Wide-Web, FTP, and email. Just like these standards are"neutral", we also aim to endow the VNS with"wake neutrality"so that each participant can develop its own digital voice. We focus on voice as a starting point to talk to any IoT object and explain briefly how the VNS may be expanded to other AI technologies enabling person-to-machine conversations (really machine-to-machine), including computer vision or neural interfaces. We also describe briefly considerations for a broader set of standards, MIT Open AI (MOA), including a reference architecture to serve as a starting point for the development of a general conversational commerce infrastructure that has standard"Wake Words", NLP commands such as"Shopping Lists"or"Flash Card Reviews", and personalities such as Pi or 271. Privacy and security are key elements considered because of speech-to-text errors and the amount of personal information contained in a voice sample.

Ferran Hueto

Papers

COVID-19 Artificial Intelligence Diagnosis Using Only Cough Recordings

Digital imaging biomarkers feed machine learning for melanoma screening.

Hi Sigma, do I have the Coronavirus?: Call for a New Artificial Intelligence Approach to Support Health Care Professionals Dealing With The COVID-19 Pandemic.

The MIT Voice Name System