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  1. Article ; Online: Covid-19 Imaging Tools: How Big Data is Big?

    Santosh, K C / Ghosh, Sourodip

    Journal of medical systems

    2021  Volume 45, Issue 7, Page(s) 71

    Abstract: In this paper, considering year 2020 and Covid-19, we analyze medical imaging tools and ... learning in the deep learning era; and iv) data augmentation. Medical imaging tools do not explicitly ... driven tools that employ two different types of image data, namely chest Computed Tomography (CT) and ...

    Abstract In this paper, considering year 2020 and Covid-19, we analyze medical imaging tools and their performance scores in accordance with the dataset size and their complexity. For this, we mainly consider AI-driven tools that employ two different types of image data, namely chest Computed Tomography (CT) and X-ray. We elaborate on their strengths and weaknesses by taking the following important factors into account: i) dataset size; ii) model fitting criteria (over-fitting and under-fitting); iii) transfer learning in the deep learning era; and iv) data augmentation. Medical imaging tools do not explicitly analyze model fitting. Also, using transfer learning, with fewer data, one could possibly build Covid-19 deep learning model but they are limited to education and training. We observe that, in both image modalities, neither the dataset size nor does data augmentation work well for Covid-19 screening purposes because a large dataset does not guarantee all possible Covid-19 manifestations and data augmentation does not create new Covid-19 cases.
    MeSH term(s) Big Data ; COVID-19/diagnostic imaging ; Deep Learning ; Humans ; Radiography, Thoracic ; Tomography, X-Ray Computed
    Language English
    Publishing date 2021-06-03
    Publishing country United States
    Document type Journal Article
    ZDB-ID 423488-1
    ISSN 1573-689X ; 0148-5598
    ISSN (online) 1573-689X
    ISSN 0148-5598
    DOI 10.1007/s10916-021-01747-2
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Sonographic Diagnosis of COVID-19: A Review of Image Processing for Lung Ultrasound.

    McDermott, Conor / Łącki, Maciej / Sainsbury, Ben / Henry, Jessica / Filippov, Mihail / Rossa, Carlos

    Frontiers in big data

    2021  Volume 4, Page(s) 612561

    Abstract: ... for unspecialized personnel. This paper reviews how lung ultrasound (LUS) imaging can be used for COVID-19 diagnosis ... of COVID-19 in LUS images. Then, the paper reviews how general lung ultrasound examinations are performed ... before addressing how COVID-19 manifests itself in the images. This will provide the basis to study contemporary ...

    Abstract The sustained increase in new cases of COVID-19 across the world and potential for subsequent outbreaks call for new tools to assist health professionals with early diagnosis and patient monitoring. Growing evidence around the world is showing that lung ultrasound examination can detect manifestations of COVID-19 infection. Ultrasound imaging has several characteristics that make it ideally suited for routine use: small hand-held systems can be contained inside a protective sheath, making it easier to disinfect than X-ray or computed tomography equipment; lung ultrasound allows triage of patients in long term care homes, tents or other areas outside of the hospital where other imaging modalities are not available; and it can determine lung involvement during the early phases of the disease and monitor affected patients at bedside on a daily basis. However, some challenges still remain with routine use of lung ultrasound. Namely, current examination practices and image interpretation are quite challenging, especially for unspecialized personnel. This paper reviews how lung ultrasound (LUS) imaging can be used for COVID-19 diagnosis and explores different image processing methods that have the potential to detect manifestations of COVID-19 in LUS images. Then, the paper reviews how general lung ultrasound examinations are performed before addressing how COVID-19 manifests itself in the images. This will provide the basis to study contemporary methods for both segmentation and classification of lung ultrasound images. The paper concludes with a discussion regarding practical considerations of lung ultrasound image processing use and draws parallels between different methods to allow researchers to decide which particular method may be best considering their needs. With the deficit of trained sonographers who are working to diagnose the thousands of people afflicted by COVID-19, a partially or totally automated lung ultrasound detection and diagnosis tool would be a major asset to fight the pandemic at the front lines.
    Language English
    Publishing date 2021-03-09
    Publishing country Switzerland
    Document type Journal Article ; Review
    ISSN 2624-909X
    ISSN (online) 2624-909X
    DOI 10.3389/fdata.2021.612561
    Database MEDical Literature Analysis and Retrieval System OnLINE

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