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Heart DT: Monitoring and Preventing Cardiac Pathologies Using AI and IoT Sensors

Author

Listed:
  • Roberta Avanzato

    (Department of Electrical, Electronic and Computer Engineering, University of Catania, Viale Andrea Doria, 95125 Catania, Italy)

  • Francesco Beritelli

    (Department of Electrical, Electronic and Computer Engineering, University of Catania, Viale Andrea Doria, 95125 Catania, Italy)

  • Alfio Lombardo

    (Department of Electrical, Electronic and Computer Engineering, University of Catania, Viale Andrea Doria, 95125 Catania, Italy)

  • Carmelo Ricci

    (Department of Electrical, Electronic and Computer Engineering, University of Catania, Viale Andrea Doria, 95125 Catania, Italy
    CNIT—Research Unit of the Catania, National Inter-University Consortium for Telecommunications, 43124 Parma, Italy)

Abstract

Today’s healthcare facilities require new digital tools to cope with the rapidly increasing demand for technology that can support healthcare operators. The advancement of technology is leading to the pervasive use of IoT devices in daily life, capable of acquiring biomedical and biometric parameters, and providing an opportunity to activate new tools for the medical community. Digital twins (DTs) are a form of technology that are gaining more prominence in these scenarios. Many scientific research papers in the literature are combining artificial intelligence (AI) with DTs. In this work, we propose a case study including a proof of concept based on microservices, the heart DT, for the evaluation of electrocardiogram (ECG) signals by means of an artificial intelligence component. In addition, a higher-level platform is presented and described for the complete management and monitoring of cardiac pathologies. The overall goal is to provide a system that can facilitate the patient–doctor relationship, improve medical treatment times, and reduce costs.

Suggested Citation

  • Roberta Avanzato & Francesco Beritelli & Alfio Lombardo & Carmelo Ricci, 2023. "Heart DT: Monitoring and Preventing Cardiac Pathologies Using AI and IoT Sensors," Future Internet, MDPI, vol. 15(7), pages 1-16, June.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:7:p:223-:d:1176921
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    References listed on IDEAS

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    1. Tuuli Katarina Lepasepp & William Hurst, 2021. "A Systematic Literature Review of Industry 4.0 Technologies within Medical Device Manufacturing," Future Internet, MDPI, vol. 13(10), pages 1-19, October.
    2. Raihan Ur Rasool & Hafiz Farooq Ahmad & Wajid Rafique & Adnan Qayyum & Junaid Qadir & Zahid Anwar, 2023. "Quantum Computing for Healthcare: A Review," Future Internet, MDPI, vol. 15(3), pages 1-36, February.
    3. Samia Melhem, 2019. "Assessing Country Progress Towards Digitization," World Bank Publications - Reports 31305, The World Bank Group.
    4. A. R. Al-Ali & Ragini Gupta & Tasneem Zaman Batool & Taha Landolsi & Fadi Aloul & Ahmad Al Nabulsi, 2020. "Digital Twin Conceptual Model within the Context of Internet of Things," Future Internet, MDPI, vol. 12(10), pages 1-15, September.
    5. Attila Csaba Marosi & Márk Emodi & Ákos Hajnal & Róbert Lovas & Tamás Kiss & Valerie Poser & Jibinraj Antony & Simon Bergweiler & Hamed Hamzeh & James Deslauriers & József Kovács, 2022. "Interoperable Data Analytics Reference Architectures Empowering Digital-Twin-Aided Manufacturing," Future Internet, MDPI, vol. 14(4), pages 1-19, April.
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    Cited by:

    1. Massimo Cafaro & Italo Epicoco & Marco Pulimeno, 2024. "State-of-the-Art Future Internet Technology in Italy 2022–2023," Future Internet, MDPI, vol. 16(2), pages 1-4, February.

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