ABSTRACT
Lung ultrasound with an artificial intelligence (AI) application provides a low-cost, non-invasive diagnostic that can play a supporting role in diagnosing COVID-19, especially in areas without PCR/CT access. [1][2] Especially throughout the COVID-19 pandemic fast, safe and highly sensitive diagnostic tools are crucial. [3] The goal of this work was twofold: 1. create a publicly available dataset of lung ultrasound images/videos and 2. train an AI algorithm to detect and classify COVID-19 on lung ultrasound images and videos. The largest publicly available COVID-19 lung ultrasound dataset was created from a variety of sources, with > 200 videos and > 50 images. The dataset is heterogeneous, mostly acquired with a convex transducer and according to BLUE protocol. Using available additional patient information, lung ultrasound images in the dataset were categorized as COVID-19, bacterial pneumonia, other viral pneumonia, and healthy. In addition, two independent reviewers evaluated the visible pathologies in the lung ultrasound images. On the dataset, an in-depth study of deep learning methods for differential diagnosis of lung pathologies was performed. In the COVID-19 ultrasound images and videos lung ultrasound signs of a nonspecific pneuomia (fragmented pleural lines, B-lines, (subpleural) consolidations, aero bronchograms and pleural effusions) were visible.The frame-based model correctly distinguished COVID-19 lung ultrasound images from healthy and bacterial pneumonia with a sensitivity of 0.90 ± 0.08 and a specificity of 0.96 ± 0.04. Our work shows promising results of AI application in the field of lung sonography using COVID-19 as an example. Currently, the AI model is in the clinical trial phase. The data set as well as the code for the CNN are publicly available: https://github.com/BorgwardtLab/covid19%5fultrasound. The provided dataset facilitates the validation of lung ultrasound based neural networks to develop fast, accessible screening methods for pulmonary diseases. [ FROM AUTHOR] Copyright of Ultrasound in Medicine & Biology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
As the biomedical community produces datasets that are increasingly complex and high dimensional, there is a need for more sophisticated computational tools to extract biological insights. We present Multiscale PHATE, a method that sweeps through all levels of data granularity to learn abstracted biological features directly predictive of disease outcome. Built on a coarse-graining process called diffusion condensation, Multiscale PHATE learns a data topology that can be analyzed at coarse resolutions for high-level summarizations of data and at fine resolutions for detailed representations of subsets. We apply Multiscale PHATE to a coronavirus disease 2019 (COVID-19) dataset with 54 million cells from 168 hospitalized patients and find that patients who die show CD16hiCD66blo neutrophil and IFN-γ+ granzyme B+ Th17 cell responses. We also show that population groupings from Multiscale PHATE directly fed into a classifier predict disease outcome more accurately than naive featurizations of the data. Multiscale PHATE is broadly generalizable to different data types, including flow cytometry, single-cell RNA sequencing (scRNA-seq), single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq), and clinical variables.
Subject(s)
COVID-19 , Single-Cell Analysis , Chromatin , Humans , Single-Cell Analysis/methods , Transposases , Exome SequencingABSTRACT
INTRODUCTION: Since December 2019, a novel coronavirus (SARS-CoV-2) has triggered a world-wide pandemic with an enormous medical and societal-economic toll. Thus, our aim was to gather all available information regarding comorbidities, clinical signs and symptoms, outcomes, laboratory findings, imaging features, and treatments in patients with coronavirus disease 2019 (COVID-19). METHODS: EMBASE, PubMed/Medline, Scopus, and Web of Science were searched for studies published in any language between December 1st, 2019 and March 28th, 2020. Original studies were included if the exposure of interest was an infection with SARS-CoV-2 or confirmed COVID-19. The primary outcome was the risk ratio of comorbidities, clinical signs and symptoms, laboratory findings, imaging features, treatments, outcomes, and complications associated with COVID-19 morbidity and mortality. We performed random-effects pairwise meta-analyses for proportions and relative risks, I2, T2, and Cochrane Q, sensitivity analyses, and assessed publication bias. RESULTS: 148 studies met the inclusion criteria for the systematic review and meta-analysis with 12'149 patients (5'739 female) and a median age of 47.0 [35.0-64.6] years. 617 patients died from COVID-19 and its complication. 297 patients were reported as asymptomatic. Older age (SMD: 1.25 [0.78-1.72]; p < 0.001), being male (RR = 1.32 [1.13-1.54], p = 0.005) and pre-existing comorbidity (RR = 1.69 [1.48-1.94]; p < 0.001) were identified as risk factors of in-hospital mortality. The heterogeneity between studies varied substantially (I2; range: 1.5-98.2%). Publication bias was only found in eight studies (Egger's test: p < 0.05). CONCLUSIONS: Our meta-analyses revealed important risk factors that are associated with severity and mortality of COVID-19.