Fig. 1

The machine learning workflow. Input parameters included lung mechanics at PEEP 5 cmH₂O (M5), lung mechanics at PEEP 15 cmH₂O (M15), respiratory partitioned mechanics (RPM), gas exchange measured at PEEP 5 cmH₂O (G5), gas exchange measured at PEEP 15 cmH₂O (G15), CT imaging acquired at PEEP 5 cmH₂O (CT5). A grid search strategy with a stratified fivefold cross-validation repeated 10 times was performed to optimize algorithms’ parameters, for both feature selection and model training. The hold-out test set was used to test the re-trained models in terms of mean area under the receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity