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      AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app

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          Abstract

          Background

          The inability to test at scale has become humanity's Achille's heel in the ongoing war against the COVID-19 pandemic. A scalable screening tool would be a game changer. Building on the prior work on cough-based diagnosis of respiratory diseases, we propose, develop and test an Artificial Intelligence (AI)-powered screening solution for COVID-19 infection that is deployable via a smartphone app. The app, named AI4COVID-19 records and sends three 3-s cough sounds to an AI engine running in the cloud, and returns a result within 2 min.

          Methods

          Cough is a symptom of over thirty non-COVID-19 related medical conditions. This makes the diagnosis of a COVID-19 infection by cough alone an extremely challenging multidisciplinary problem. We address this problem by investigating the distinctness of pathomorphological alterations in the respiratory system induced by COVID-19 infection when compared to other respiratory infections. To overcome the COVID-19 cough training data shortage we exploit transfer learning. To reduce the misdiagnosis risk stemming from the complex dimensionality of the problem, we leverage a multi-pronged mediator centered risk-averse AI architecture.

          Results

          Results show AI4COVID-19 can distinguish among COVID-19 coughs and several types of non-COVID-19 coughs. The accuracy is promising enough to encourage a large-scale collection of labeled cough data to gauge the generalization capability of AI4COVID-19. AI4COVID-19 is not a clinical grade testing tool. Instead, it offers a screening tool deployable anytime, anywhere, by anyone. It can also be a clinical decision assistance tool used to channel clinical-testing and treatment to those who need it the most, thereby saving more lives.

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          Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks

          Ioannis Apostolopoulos, Tzani Mpesiana (2020)
          In this study, a dataset of X-ray images from patients with common bacterial pneumonia, confirmed Covid-19 disease, and normal incidents, was utilized for the automatic detection of the Coronavirus disease. The aim of the study is to evaluate the performance of state-of-the-art convolutional neural network architectures proposed over the recent years for medical image classification. Specifically, the procedure called Transfer Learning was adopted. With transfer learning, the detection of various abnormalities in small medical image datasets is an achievable target, often yielding remarkable results. The datasets utilized in this experiment are two. Firstly, a collection of 1427 X-ray images including 224 images with confirmed Covid-19 disease, 700 images with confirmed common bacterial pneumonia, and 504 images of normal conditions. Secondly, a dataset including 224 images with confirmed Covid-19 disease, 714 images with confirmed bacterial and viral pneumonia, and 504 images of normal conditions. The data was collected from the available X-ray images on public medical repositories. The results suggest that Deep Learning with X-ray imaging may extract significant biomarkers related to the Covid-19 disease, while the best accuracy, sensitivity, and specificity obtained is 96.78%, 98.66%, and 96.46% respectively. Since by now, all diagnostic tests show failure rates such as to raise concerns, the probability of incorporating X-rays into the diagnosis of the disease could be assessed by the medical community, based on the findings, while more research to evaluate the X-ray approach from different aspects may be conducted.
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            Coronavirus Disease 2019 (COVID-19): Role of Chest CT in Diagnosis and Management

            Yan. Li, Liming Xia (2020)
            OBJECTIVE. The objective of our study was to determine the misdiagnosis rate of radiologists for coronavirus disease 2019 (COVID-19) and evaluate the performance of chest CT in the diagnosis and management of COVID-19. The CT features of COVID-19 are reported and compared with the CT features of other viruses to familiarize radiologists with possible CT patterns. MATERIALS AND METHODS. This study included the first 51 patients with a diagnosis of COVID-19 infection confirmed by nucleic acid testing (23 women and 28 men; age range, 26-83 years) and two patients with adenovirus (one woman and one man; ages, 58 and 66 years). We reviewed the clinical information, CT images, and corresponding image reports of these 53 patients. The CT images included images from 99 chest CT examinations, including initial and follow-up CT studies. We compared the image reports of the initial CT study with the laboratory test results and identified CT patterns suggestive of viral infection. RESULTS. COVID-19 was misdiagnosed as a common infection at the initial CT study in two inpatients with underlying disease and COVID-19. Viral pneumonia was correctly diagnosed at the initial CT study in the remaining 49 patients with COVID-19 and two patients with adenovirus. These patients were isolated and obtained treatment. Ground-glass opacities (GGOs) and consolidation with or without vascular enlargement, interlobular septal thickening, and air bronchogram sign are common CT features of COVID-19. The The "reversed halo" sign and pulmonary nodules with a halo sign are uncommon CT features. The CT findings of COVID-19 overlap with the CT findings of adenovirus infection. There are differences as well as similarities in the CT features of COVID-19 compared with those of the severe acute respiratory syndrome. CONCLUSION. We found that chest CT had a low rate of missed diagnosis of COVID-19 (3.9%, 2/51) and may be useful as a standard method for the rapid diagnosis of COVID-19 to optimize the management of patients. However, CT is still limited for identifying specific viruses and distinguishing between viruses.
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              Classification of Cough as a Symptom in Adults and Management Algorithms

              Richard Irwin, Cynthia L. French, Anne B. Chang … (2018)
              We performed systematic reviews using the population, intervention, comparison, outcome (PICO) format to answer the following key clinical question: Are the CHEST 2006 classifications of acute, subacute and chronic cough and associated management algorithms in adults that were based on durations of cough useful?
              Article 0 comments Cited 117 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Muhammad Nabeel
                Journal
                Journal ID (nlm-ta): Inform Med Unlocked
                Journal ID (iso-abbrev): Inform Med Unlocked
                Title: Informatics in Medicine Unlocked
                Publisher: The Authors. Published by Elsevier Ltd.
                ISSN (Electronic): 2352-9148
                Publication date PMC-release: 26 June 2020
                Publication date (Electronic): 26 June 2020
                Electronic Location Identifier: 100378
                Affiliations
                [a ]AI4Networks Research Center, Dept. of Electrical & Computer Engineering, University of Oklahoma, USA
                [b ]AI4Lyf LLC, USA
                [c ]Kharkiv National Medical University, Ukraine
                [d ]Dept. of Computer Science & Engineering, Michigan State University, USA
                [e ]Allergy, Asthma & Immunology Center PC, USA
                Author notes
                [∗ ]Corresponding author. muhmd.nabeel@ 123456ou.edu
                Article
                Publisher ID: S2352-9148(20)30302-6 Publisher ID: 100378
                DOI: 10.1016/j.imu.2020.100378
                PMC ID: 7318970
                PubMed ID: 32839734
                SO-VID: 84c045e7-7ed3-4264-bdbe-fcf249d826dc
                Copyright © © 2020 The Authors. Published by Elsevier Ltd.
                License:

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                Date received : 4 May 2020
                Date revision received : 19 June 2020
                Date accepted : 19 June 2020
                Categories
                Subject: Article

                Keywords: artificial intelligence,covid-19,preliminary medical diagnosis,pre-screening,public healthcare
                Data availability:
                Keywords: artificial intelligence, covid-19, preliminary medical diagnosis, pre-screening, public healthcare

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