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

Objective.Manual end-inspiratory pause maneuvers (EIPM) for plateau pressure (P_plat) measurement suffer from significant variability, yet no objective assessment tool exists. This study develops an automated framework to evaluate EIPM reliability during mechanical ventilation.Approach.We proposed an automated framework in which a one-dimensional convolutional neural network (1DCNN) was developed to extract discriminative features from pressure, flow, and volume waveforms. This model accurately classified candidate reliable EIPMs, unreliable EIPMs, and non-EIPM events. For candidate reliable EIPMs, the method subsequently calculated the hold time to definitively identify reliable EIPMs (hold time ⩾2 s) and hold-time subthreshold EIPMs (hold time <2 s).Main Results.The 1DCNN model demonstrated robust classification performance, achievingF1 scores of 0.935 for candidate reliable EIPMs, 0.924 for unreliable EIPMs, and 0.992 for non-EIPM events. The subsequent hold-time calculation, applied to candidate reliable EIPMs, achieved 100% sensitivity in classifying reliable and hold-time subthreshold EIPMs.Significance.The proposed method provides a reliable tool for the automated quality control of EIPM operations, holding significant potential to support a more standardized and protocolized implementation of lung-protective ventilation strategies.