Fig. 1

Study design: Model development for predicting veno-venous ECMO in ARDS patients. This figure depicts the process of developing and validating three machine learning models for predicting the need for vv-ECMO in ARDS patients. In the feature extraction phase, 592 imaging features were extracted through automated CT segmentation and quantitative image analysis (A), and five clinical features were obtained from electronic health records (B). During the feature selection phase, relevant features were selected. For imaging, a multi-step feature selection process including clustering, cross-validated Minimum Redundancy Maximum Relevance (MRMR) ranking and correlation analysis were performed (C). Clinical parameters were assessed for selection on the basis of correlation analysis (D). The selected features formed the basis for the training of the Imaging Model (E) and the Clinical Model (G). These feature sets were then combined to train the Combined Model (F). Finally, all the models were validated in the validation cohort (H).