Development and validation of an explainable machine learning model integrating modified CT criteria for predicting...

The diagnosis of posttraumatic acute diffuse brain swelling (ADBS) poses a considerable clinical challenge. This study aimed to propose modified computed tomography criteria for accurately diagnosing posttraumatic ADBS and subsequently to develop and validate risk prediction models using machine learning (ML) algorithms.

This retrospective multicenter study included 356 patients with traumatic brain injury from two medical centers, comprising 226 patients for model training and 130 for external validation. Posttraumatic ADBS was defined as the combination of diffuse injury type III and sulcal effacement at the vertex. Five ML algorithms were trained to develop prediction models for ADBS, with predictors identified using Least Absolute Shrinkage and Selection Operator regression and the Boruta algorithm. Model performance was evaluated using appropriate metrics to determine the optimal model, which was validated internally by cross-validation and externally in the validation cohort. The best-performing model was interpreted using SHapley Additive exPlanations (SHAP) and deployed as a simple risk calculator.

A total of 65 patients (18.3%) developed posttraumatic ADBS. Using four key predictors, the random forest (RF) model demonstrated excellent discriminative performance in both the derivation cohort (area under the receiver-operating-characteristic curve [AUC] = 0.973) and the external validation cohort (AUC = 0.957). The mean AUCs from five-fold and ten-fold internal cross-validation were 0.863 ± 0.042 and 0.859 ± 0.077, respectively. SHAP analysis provided global and local interpretability for the model, which has been deployed as a publicly available web-based risk calculator.

The developed RF model shows potential for assessing the risk of posttraumatic ADBS, thereby contributing to clinical decision-making.