Artificial Intelligence for COVID-19 Detection in Medical Imaging—Diagnostic Measures and Wasting—A Systematic Umbrella Review

Article activity feed

1. Version published to 10.3390/jcm11072054

   Apr 6, 2022
2. 

   ScreenIT

   May 7, 2021

   SciScore for 10.1101/2021.05.03.21256565: (What is this?)

   Please note, not all rigor criteria are appropriate for all manuscripts.

   Table 1: Rigor

   Ethics	not detected.
   Sex as a biological variable	not detected.
   Randomization	not detected.
   Blinding	not detected.
   Power Analysis	not detected.

   Table 2: Resources

   Antibodies
   Sentences	Resources
   We excluded these primary studies that used reference standards other than assay types (NAATs, antigen tests, and antibody tests) from nasopharyngeal or oropharyngeal swab samples, nasal aspirate, nasal wash or saliva, sputum or tracheal aspirate, or bronchoalveolar lavage (BAL) [16, 34].	antigen tests, suggested: None
   Software and Algorithms
   Sentences	Resources
   2.2 Search methods: In order to determine whether there are any eligible papers, we conducted a pre-search in the middle of August 2020 via Google Scholar by …

   SciScore for 10.1101/2021.05.03.21256565: (What is this?)

   Please note, not all rigor criteria are appropriate for all manuscripts.

   Table 1: Rigor

   Ethics	not detected.
   Sex as a biological variable	not detected.
   Randomization	not detected.
   Blinding	not detected.
   Power Analysis	not detected.

   Table 2: Resources

   Antibodies
   Sentences	Resources
   We excluded these primary studies that used reference standards other than assay types (NAATs, antigen tests, and antibody tests) from nasopharyngeal or oropharyngeal swab samples, nasal aspirate, nasal wash or saliva, sputum or tracheal aspirate, or bronchoalveolar lavage (BAL) [16, 34].	antigen tests, suggested: None
   Software and Algorithms
   Sentences	Resources
   2.2 Search methods: In order to determine whether there are any eligible papers, we conducted a pre-search in the middle of August 2020 via Google Scholar by browsing.	Google Scholar suggested: (Google Scholar, RRID:SCR_008878)
   We searched seven article databases (MEDLINE, EMBASE, Web of Science, Scopus, dblp, Cochrane Library, IEEE Xplore) and two preprint databases (arXiv, OSF Preprints) from inception to 01 September 2020 using predefined search strategies.	EMBASE suggested: (EMBASE, RRID:SCR_001650) Cochrane Library suggested: (Cochrane Library, RRID:SCR_013000)
   In developing the search strategy for MEDLINE, we combined the Medical Subject Headings (MeSH) and full-text words.	MEDLINE suggested: (MEDLINE, RRID:SCR_002185) MeSH suggested: (MeSH, RRID:SCR_004750)

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   Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

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   Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

   4.1 Study Strengths & Limitations: Our umbrella review has the following strengths. First, the search strategy was comprehensive based on adequate inclusion criteria related to research question, and spanned across a wide selection of existing data sources: papers and preprints. This selection was further expanded by searching the references of included papers to identify new works. Noteworthy, the searches were not limited in terms of format or language. The process of our review was rigorous as the study was proceeded by the publication of protocol. We used the most up-to-date and applicable tools to assess credibility and quality of reporting – AMSTAR 2 and PRISMA with extension for DTA, respectively. Nevertheless, these instruments have been designed for reviews in the fields of medicine and health sciences, where the formulation of the research question is structured, the methodology is validated and the quantitative synthesis of results is more popular. There are also other limitations associated with this study. Although our exhaustive and sensitive search covered multiple aspects, some studies might have still been missed. We did not search for Chinese data sources that could include many valuable papers. Secondly, it must be noted that the vast majority of the included studies focused on a broader context than purely diagnosing COVID-19 from medical images. In this study, we have investigated wasting among the reviews. We based on the date of publishing of the last i...

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   Results from TrialIdentifier: No clinical trial numbers were referenced.

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   Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

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   Results from JetFighter: We did not find any issues relating to colormaps.

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   Results from rtransparent:

   • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
   • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
   • No protocol registration statement was detected.

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   Results from scite Reference Check: We found no unreliable references.

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   Read the original source
3. 

   Version published to 10.1101/2021.05.03.21256565v1 on medRxiv

   May 5, 2021