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Related Concept Videos

Mechanical Ventilation II: Invasive Ventilation01:23

Mechanical Ventilation II: Invasive Ventilation

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Ventilators are essential medical equipment used to aid patients with respiratory difficulties. Their primary function is to assist or replace spontaneous breathing by providing mechanical ventilation. There are two general classes of mechanical ventilators: negative-pressure and positive-pressure ventilators.
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Mechanical ventilators are life-saving devices that support or replace spontaneous breathing. They deliver breaths to patients through varying methods known as ventilator modes. Understanding these modes is critical for healthcare providers managing patients with respiratory failure.
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Mechanical ventilation is a life-saving technique for managing acute respiratory failure and other respiratory complications. The process involves using a machine known as a ventilator to supply oxygen to the lungs and assist in removing carbon dioxide. It serves as a bridge to long-term mechanical ventilation or a temporary measure until ventilatory support is discontinued. The ventilator can maintain this function for a prolonged period, providing critical support for patients until they can...
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Mechanical Ventilation III: Noninvasive Ventilation01:23

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Noninvasive positive-pressure ventilation (NIPPV), continuous positive airway pressure (CPAP), and bilevel positive airway pressure (BiPAP) are essential methods in respiratory care. These ventilation techniques offer unique benefits for patients with various respiratory conditions, providing adequate support without requiring intubation. Let's explore how each method is crucial in improving patient outcomes and enhancing respiratory therapy.
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Assessment of Ventilation I: Respiratory Rate01:20

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

Jaroslav Pažout1, Milan Němý2, Jakub Mikeš1

  • 1Department 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.

Computer Methods and Programs in Biomedicine
|June 29, 2025
PubMed
Summary
This summary is machine-generated.

A new AI system, SmartAlert, accurately detects and classifies patient-ventilator asynchronies (PVAs) in real-time from ventilator screen data. This technology aims to improve patient care by reducing alarm fatigue and optimizing ventilator settings.

Keywords:
Deep neural networksMechanical ventilationPatient-ventilator asynchronyReal-time monitoring

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Area of Science:

  • Critical Care Medicine
  • Biomedical Engineering
  • Artificial Intelligence in Healthcare

Background:

  • Patient-ventilator asynchronies (PVAs) are linked to increased mortality and ventilator-induced lung injury.
  • Current PVA detection methods are limited by static thresholds, complex preprocessing, or proprietary data access.
  • There is a need for real-time, automated PVA detection systems.

Purpose of the Study:

  • To develop and validate SmartAlert, an online, real-time system for detecting and classifying PVAs.
  • To classify PVA severity and alert clinicians promptly.
  • To utilize readily available ventilator screen data for PVA analysis.

Main Methods:

  • Developed the SmartAlert system using ICU patient ventilator screen recordings.
  • Extracted pressure and flow waveforms from video, converting them to time-series data.
  • Employed deep neural networks for PVA classification and alarm level assignment, validated against expert consensus.

Main Results:

  • SmartAlert achieved high accuracy in alarm level prediction (83.8%) and PVA classification (89.3%).
  • The system demonstrated strong performance with weighted AUC-ROC values of 0.943 for alarms and 0.951 for PVAs.
  • High specificity was observed for urgent alarms (99.9%) and specific PVA types (e.g., ineffective triggering 98.5%).

Conclusions:

  • SmartAlert is an automated, validated system for real-time PVA detection, classification, and clinician alerting.
  • The system shows potential to reduce alarm fatigue and optimize ventilator management.
  • Further clinical trials are needed to confirm its impact on patient outcomes.