SmartAlert: Machine learning-based patient-ventilator asynchrony detection system in intensive care units.

Journal: Computer methods and programs in biomedicine
Published Date: Jun 21, 2025

Abstract

BACKGROUND AND OBJECTIVE: Patient-ventilator asynchronies (PVA) are associated with ventilator-induced lung injury and increased mortality. Current detection methods rely on static thresholds, extensive preprocessing, or proprietary ventilator data. This study aimed to develop and validate a fully online, real-time system that detects and classifies PVAs directly from ventilator screen data while alerting clinicians based on severity.

Authors

  • Jaroslav Pažout
    Department of Anesthesiology and Intensive Care, 3rd Faculty of Medicine, Charles University and Kralovske Vinohrady University Hospital in Prague, Šrobárova 50, Prague 100 34, Czech Republic.
  • Milan Němý
    Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Blickagången 16, Huddinge 14183, Sweden; Department of Cognitive Systems and Neurosciences, Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslávských partyzánů 1580/3, Prague 160 00, Czech Republic. Electronic address: milan.nemy@cvut.cz.
  • Jakub Mikeš
    Department of Anesthesiology and Intensive Care, 3rd Faculty of Medicine, Charles University and Kralovske Vinohrady University Hospital in Prague, Šrobárova 50, Prague 100 34, Czech Republic.
  • Jan Jirman
    Department of Cognitive Systems and Neurosciences, Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslávských partyzánů 1580/3, Prague 160 00, Czech Republic.
  • Jan Kubr
    Department of Computer Graphics and Interaction, Faculty of Electrical Engineering, Czech Technical University in Prague, Karlovo náměstí 13, Prague 121 35, Czech Republic.
  • Eliška Niebauerová
    Department of Anesthesiology and Intensive Care, 3rd Faculty of Medicine, Charles University and Kralovske Vinohrady University Hospital in Prague, Šrobárova 50, Prague 100 34, Czech Republic.
  • Miroslav Macík
    Department of Computer Graphics and Interaction, Faculty of Electrical Engineering, Czech Technical University in Prague, Karlovo náměstí 13, Prague 121 35, Czech Republic.
  • Michal Pech
    Department of Cognitive Systems and Neurosciences, Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslávských partyzánů 1580/3, Prague 160 00, Czech Republic.
  • Michal Štajnrt
    Department of Anesthesiology and Intensive Care, 3rd Faculty of Medicine, Charles University and Kralovske Vinohrady University Hospital in Prague, Šrobárova 50, Prague 100 34, Czech Republic.
  • Jakub Vaněk
    Department of Cognitive Systems and Neurosciences, Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslávských partyzánů 1580/3, Prague 160 00, Czech Republic.
  • Petr Waldauf
    The Third Faculty of Medicine, Charles University, Prague, Czech Republic.
  • Václav Zvoníček
    Department of Anesthesiology and Intensive Care, 3rd Faculty of Medicine, Charles University and Kralovske Vinohrady University Hospital in Prague, Šrobárova 50, Prague 100 34, Czech Republic.
  • Lenka Vysloužilová
    Department of Cognitive Systems and Neurosciences, Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslávských partyzánů 1580/3, Prague 160 00, Czech Republic.
  • Robert Babuška
  • Frantisek Duska
    The Third Faculty of Medicine, Charles University, Prague, Czech Republic.

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