ARTIFICIAL INTELLIGENCE TOOLS FOR EFFECTIVE MONITORING OF POPULATION AT DISTANCE DURING COVID-19 PANDEMIC. RESULTS FROM AN ITALIAN PILOT FEASIBILITY STUDY (RICOVAI-19 STUDY)

  1. Marco Mazzanti
  2. Aldo Salvi
  3. Stefania Giacomini
  4. Elisabetta Perazzini
  5. Cinzia Nitti
  6. Susanna Contucci
  7. Massimo D’Angelo
  8. Danilo Ballanti
  9. Domenico Ursino
  10. Matteo Marcosignori
  11. Raniero Romagnoli
  12. Marcello Tavio
  13. Andrea Giacometti
  14. Serena Tomassetti
  15. Riccardo Bonazzi
  16. Matteo Maccioni
  17. Lina Zuccatosta
  18. RICOVAI-19 Study

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Abstract

In order to reduce the burden on healthcare systems and in particular to support an appropriate way to the Emergency Department (ED) access, home tele-monitoring patients was strongly recommended during the COVID-19 pandemic. Furthermore, paper from numerous groups has shown the potential of using data from wearable devices to characterize each individual’s unique baseline, identify deviations from that baseline suggestive of a viral infection, and to aggregate that data to better inform population surveillance trends. However, no evidence about usage of Artificial Intelligence (AI) applicatives on digitally data collected from patients and doctors exists. With a growing global population of connected wearable users, this could potentially help to improve the earlier diagnosis and management of infectious individuals and improving timeliness and precision of tracking infectious disease outbreaks.

During the study RICOVAI-19 ( RICOV ero ospedaliero con strumenti di A rtificial Intelligence nei pazienti con COV id-19) performed in the Marche Region, Italy, we evaluated 129 subjects monitored at home in a six-months period between March 22, 2021 and October 22, 2021. During the monitoring, personal on demand health technologies were used to collect clinical and vital data in order to feed the database and the machine learning engine. The AI output resulted in a clinical stability index (CSI) which enables the system to deliver suggestions to the population and doctors about how intervene.

Results showed the beneficial influence of CSI for predicting clinical classes of subjects and identifying who of them need to be admitted at ED. The same pattern of results was confirming the alert included in the decision support system in order to request further testing or clinical information in some cases.

In conclusion, our study does support a high impact of AI tools on COVID-19 outcomes to fight this pandemic by driving new approaches to public awareness.

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    SciScore for 10.1101/2022.02.04.22270087: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.
    • No funding statement was detected.
    • No protocol registration statement was detected.

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

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  2. Version published to 10.1101/2022.02.04.22270087 on medRxiv