Effective deep learning approaches for predicting COVID-19 outcomes from chest computed tomography volumes.

The rapid evolution of the novel coronavirus disease (COVID-19) pandemic has resulted in an urgent need for effective clinical tools to reduce transmission and manage severe illness. Numerous teams are quickly developing artificial intelligence approaches to these problems, including using deep learning to predict COVID-19 diagnosis and prognosis from chest computed tomography (CT) imaging data. In this work, we assess the value of aggregated chest CT data for COVID-19 prognosis compared to clinical metadata alone. We develop a novel patient-level algorithm to aggregate the chest CT volume into a 2D representation that can be easily integrated with clinical metadata to distinguish COVID-19 pneumonia from chest CT volumes from healthy participants and participants with other viral pneumonia. Furthermore, we present a multitask model for joint segmentation of different classes of pulmonary lesions present in COVID-19 infected lungs that can outperform individual segmentation models for each task. We directly compare this multitask segmentation approach to combining feature-agnostic volumetric CT classification feature maps with clinical metadata for predicting mortality. We show that the combination of features derived from the chest CT volumes improve the AUC performance to 0.80 from the 0.52 obtained by using patients' clinical data alone. These approaches enable the automated extraction of clinically relevant features from chest CT volumes for risk stratification of COVID-19 patients.

Capturing COVID-19-Like Symptoms at Scale Using Banner Ads on an Online News Platform: Pilot Survey Study.

BACKGROUND: Identifying new COVID-19 cases is challenging. Not every suspected case undergoes testing, because testing kits and other equipment are limited in many parts of the world. Yet populations increasingly use the internet to manage both home and work life during the pandemic, giving researchers mediated connections to millions of people sheltering in place. OBJECTIVE: The goal of this study was to assess the feasibility of using an online news platform to recruit volunteers willing to report COVID-19-like symptoms and behaviors. METHODS: An online epidemiologic survey captured COVID-19-related symptoms and behaviors from individuals recruited through banner ads offered through Microsoft News. Respondents indicated whether they were experiencing symptoms, whether they received COVID-19 testing, and whether they traveled outside of their local area. RESULTS: A total of 87,322 respondents completed the survey across a 3-week span at the end of April 2020, with 54.3% of the responses from the United States and 32.0% from Japan. Of the total respondents, 19,631 (22.3%) reported at least one symptom associated with COVID-19. Nearly two-fifths of these respondents (39.1%) reported more than one COVID-19-like symptom. Individuals who reported being tested for COVID-19 were significantly more likely to report symptoms (47.7% vs 21.5%; P<.001). Symptom reporting rates positively correlated with per capita COVID-19 testing rates (R2=0.26; P<.001). Respondents were geographically diverse, with all states and most ZIP Codes represented. More than half of the respondents from both countries were older than 50 years of age. CONCLUSIONS: News platforms can be used to quickly recruit study participants, enabling collection of infectious disease symptoms at scale and with populations that are older than those found through social media platforms. Such platforms could enable epidemiologists and researchers to quickly assess trends in emerging infections potentially before at-risk populations present to clinics and hospitals for testing and/or treatment.