Deep learning based automated assessment of end-inspiratory pause maneuver reliability in invasive mechanical ventilation

Key Points

• This research aims to develop an automated method for assessing the reliability of end-inspiratory pause maneuver during invasive mechanical ventilation.
• Developed a deep learning algorithm for assessment
• Tested the tool on various EIPM operations
• Evaluated reliability through automated quality control
• The tool proved to be a reliable assessment method
• Facilitates standardized implementation of ventilation strategies
• Promotes better adherence to lung-protective protocols