Enhancing Outcome Prediction in Intracerebral Hemorrhage Through Deep Learning: A Retrospective Multicenter Study.

RATIONALE AND OBJECTIVES: This study aimed to employ deep learning techniques to analyze and validate an automatic prognostic biomarker for predicting outcomes following intracerebral hemorrhage (ICH).

MATERIALS AND METHODS: This study included patients with ICH whose onset-to-imaging time (OIT) was less than 6 h. Patients were randomly divided into training and test sets at a 7:3 ratio. Using the Resnet50 deep learning method, we extracted features from the hematoma and perihematomal edema (PHE) areas and constructed a 90-day prognosis prediction model using logistic regression. To evaluate predictive efficacy and clinical significance, we employed logistic regression to train three models: Clinical, Deep Score, and the combined Clinical-Deep Learning (Merge).

RESULTS: Our study comprised 1098 patients (652 male, 446 female), with a mean Glasgow Coma Scale (GCS) score of 10. Univariate and multivariate analyses identified age, intraventricular hemorrhage (IVH), hematoma and PHE volume, and admission GCS score as independent prognostic factors. Additionally, 15 deep learning features were retained through LASSO regression. In the training set, the AUC values for the three models were as follows: Clinical model (0.88), Deep Score (0.91), and Merge model (0.94). In the test set, the Merge model exhibited a significantly higher AUC value than the other models. Calibration curves revealed satisfactory calibration of the Merge model nomogram in both training and test sets.

CONCLUSION: Our Merge model nomogram is an objective and effective prognostic tool, offering personalized risk assessments for 90-day functional outcomes in patients with ICH.