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  1. Article ; Online: Using artificial intelligence to improve COVID-19 rapid diagnostic test result interpretation.

    Mendels, David-A / Dortet, Laurent / Emeraud, Cécile / Oueslati, Saoussen / Girlich, Delphine / Ronat, Jean-Baptiste / Bernabeu, Sandrine / Bahi, Silvestre / Atkinson, Gary J H / Naas, Thierry

    Proceedings of the National Academy of Sciences of the United States of America

    2021  Volume 118, Issue 12

    Abstract: Serological rapid diagnostic tests (RDTs) are widely used across pathologies, often providing users ... of the COVID-19 pandemic, new RDTs for identifying SARS-CoV-2 have rapidly proliferated ... to classify SARS-CoV-2 serological RDT results and reduce reading ambiguities. Across 11 COVID-19 RDT models ...

    Abstract Serological rapid diagnostic tests (RDTs) are widely used across pathologies, often providing users a simple, binary result (positive or negative) in as little as 5 to 20 min. Since the beginning of the COVID-19 pandemic, new RDTs for identifying SARS-CoV-2 have rapidly proliferated. However, these seemingly easy-to-read tests can be highly subjective, and interpretations of the visible "bands" of color that appear (or not) in a test window may vary between users, test models, and brands. We developed and evaluated the accuracy/performance of a smartphone application (xRCovid) that uses machine learning to classify SARS-CoV-2 serological RDT results and reduce reading ambiguities. Across 11 COVID-19 RDT models, the app yielded 99.3% precision compared to reading by eye. Using the app replaces the uncertainty from visual RDT interpretation with a smaller uncertainty of the image classifier, thereby increasing confidence of clinicians and laboratory staff when using RDTs, and creating opportunities for patient self-testing.
    MeSH term(s) COVID-19/diagnosis ; COVID-19 Serological Testing ; Humans ; Machine Learning ; Mobile Applications ; SARS-CoV-2
    Language English
    Publishing date 2021-03-05
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 209104-5
    ISSN 1091-6490 ; 0027-8424
    ISSN (online) 1091-6490
    ISSN 0027-8424
    DOI 10.1073/pnas.2019893118
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1148: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  2. Article ; Online: Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks.

    Ardakani, Ali Abbasian / Kanafi, Alireza Rajabzadeh / Acharya, U Rajendra / Khadem, Nazanin / Mohammadi, Afshin

    Computers in biology and medicine

    2020  Volume 121, Page(s) 103795

    Abstract: ... 19. In this study is suggested a rapid and valid method for COVID-19 diagnosis using ... an artificial intelligence technique based. 1020 CT slices from 108 patients with laboratory proven COVID-19 (the COVID-19 ... included. Ten well-known convolutional neural networks were used to distinguish infection of COVID-19 ...

    Abstract Fast diagnostic methods can control and prevent the spread of pandemic diseases like coronavirus disease 2019 (COVID-19) and assist physicians to better manage patients in high workload conditions. Although a laboratory test is the current routine diagnostic tool, it is time-consuming, imposing a high cost and requiring a well-equipped laboratory for analysis. Computed tomography (CT) has thus far become a fast method to diagnose patients with COVID-19. However, the performance of radiologists in diagnosis of COVID-19 was moderate. Accordingly, additional investigations are needed to improve the performance in diagnosing COVID-19. In this study is suggested a rapid and valid method for COVID-19 diagnosis using an artificial intelligence technique based. 1020 CT slices from 108 patients with laboratory proven COVID-19 (the COVID-19 group) and 86 patients with other atypical and viral pneumonia diseases (the non-COVID-19 group) were included. Ten well-known convolutional neural networks were used to distinguish infection of COVID-19 from non-COVID-19 groups: AlexNet, VGG-16, VGG-19, SqueezeNet, GoogleNet, MobileNet-V2, ResNet-18, ResNet-50, ResNet-101, and Xception. Among all networks, the best performance was achieved by ResNet-101 and Xception. ResNet-101 could distinguish COVID-19 from non-COVID-19 cases with an AUC of 0.994 (sensitivity, 100%; specificity, 99.02%; accuracy, 99.51%). Xception achieved an AUC of 0.994 (sensitivity, 98.04%; specificity, 100%; accuracy, 99.02%). However, the performance of the radiologist was moderate with an AUC of 0.873 (sensitivity, 89.21%; specificity, 83.33%; accuracy, 86.27%). ResNet-101 can be considered as a high sensitivity model to characterize and diagnose COVID-19 infections, and can be used as an adjuvant tool in radiology departments.
    MeSH term(s) Adult ; Aged ; Aged, 80 and over ; Artificial Intelligence ; Betacoronavirus ; COVID-19 ; Computational Biology ; Coronavirus Infections/diagnosis ; Coronavirus Infections/diagnostic imaging ; Deep Learning ; Female ; Humans ; Lung/diagnostic imaging ; Male ; Middle Aged ; Neural Networks, Computer ; Pandemics ; Pneumonia/diagnosis ; Pneumonia/diagnostic imaging ; Pneumonia, Viral/diagnosis ; Pneumonia, Viral/diagnostic imaging ; Radiographic Image Interpretation, Computer-Assisted ; SARS-CoV-2 ; Tomography, X-Ray Computed
    Keywords covid19
    Language English
    Publishing date 2020-04-30
    Publishing country United States
    Document type Journal Article
    ZDB-ID 127557-4
    ISSN 1879-0534 ; 0010-4825
    ISSN (online) 1879-0534
    ISSN 0010-4825
    DOI 10.1016/j.compbiomed.2020.103795
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 653: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

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