Multimodal data acquisition at SARS‐CoV ‐2 drive through screening centers: Setup description and experiences in Saarland, Germany

  1. Philipp Flotho
  2. Mayur J. Bhamborae
  3. Tobias Grün
  4. Carlos Trenado
  5. David Thinnes
  6. Dominik Limbach
  7. Daniel J. Strauss

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Abstract

SARS‐CoV‐2 drive through screening centers (DTSC) have been implemented worldwide as a fast and secure way of mass screening. We use DTSCs as a platform for the acquisition of multimodal datasets that are needed for the development of remote screening methods. Our acquisition setup consists of an array of thermal, infrared and RGB cameras as well as microphones and we apply methods from computer vision and computer audition for the contactless estimation of physiological parameters. We have recorded a multimodal dataset of DTSC participants in Germany for the development of remote screening methods and symptom identification. Acquisition in the early stages of a pandemic and in regions with high infection rates can facilitate and speed up the identification of infection specific symptoms and large‐scale data acquisition at DTSC is possible without disturbing the flow of operation.

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  1. Version published to 10.1002/jbio.202000512
  2. ScreenIT

    SciScore for 10.1101/2020.12.08.20240382: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The study was approved by the responsible ethics committee (ethics commission at the Ärztekammer des Saarlandes, ID No 90/20) and after a detailed explanation of the procedure, all included participants signed a consent form.
    Consent: A total of 436 participants with signed consent form participated in our study, aged 19—86 (mean age 45.6 ± 15.2, 215 males, 221 females, 7 participants did not provide their age).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableA total of 436 participants with signed consent form participated in our study, aged 19—86 (mean age 45.6 ± 15.2, 215 males, 221 females, 7 participants did not provide their age).

    Table 2: Resources

    No key resources detected.

    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).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    A major limitation of our study is the marginal number of participants with positive PCR test result for SARS-CoV-2. The reason for this was the generally very low incidence rate in the study period in the region where the DTSC was located. In fact, the positive rate was below 0.4% at the DTSC Saarbrücken in the respective period. Thus, the specificity and sensitivity of the described approach with respect to SARS-CoV-2 infections cannot be assessed. However, our proof of concept shows that a remote data acquisition of SARS-CoV-2 infection related symptoms at DTSCs is possible. Our experiences and results enable the installation of similar approaches in regions that do massive DTSC testing. Additionally, we have recorded an unprecedented, multi-modal dataset with a high number of subjects that can be used for the development and refinement of computer vision methods. Many state-of-the-art computer vision studies with isolated modalities have to resort to smaller, publicly available datasets: Considering the development of algorithms for the detection and classification of micro-expressions, Li et al. report between 80-210 subjects for common micro-expression datasets with evoked micro-expressions and they present a dataset with 20 subjects for spontaneous micro-expressions, recorded at 100fps (RGB) and 25fps (NIR) [19]. Davison et al. record 32 subjects at 200fps for spontaneous micro-expressions [20]. Our dataset contains recordings of 436 participants at 120fps (RGB), 50fps...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

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

    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.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.12.08.20240382v1 on medRxiv