An Integrated Genetic, Molecular, and Imaging Machine Learning Model for Predicting Neurorepair Potential and Guiding Personalized Rehabilitation After Intracerebral Hemorrhage: A Multicenter Retrospective Study

Introduction Intracerebral hemorrhage (ICH) is a devastating subtype of stroke characterized by high mortality and disability rates. Objective The objective of this study is to develop and validate a machine learning (ML) predictive model integrating genetic polymorphism, serum neurotrophic factor levels, and neuroimaging features for predicting 3‐month spontaneous neurological recovery potential which is defined as favorable recovery with modified Rankin Scale (mRS) ≤ 2 without intensive rehabilitation intervention in patients with ICH and to explore its value in guiding personalized rehabilitation strategies. Methods A retrospective multicenter study was conducted in multiple tertiary hospitals. Baseline clinical data, brain‐derived neurotrophic factor (BDNF) Val66Met gene polymorphism, serum BDNF and nerve growth factor (NGF) levels, and CT/MRI imaging features were collected. The mRS score at 3 months post‐ICH was used as the primary outcome measure. Multiple ML algorithms, including logistic regression, random forest, and extreme gradient boosting (XGBoost), were applied to construct predictive models. Results The XGBoost‐based multidimensional model achieved the highest predictive efficacy, with an AUC of 0.892 (95% CI: 0.867–0.917) in the training cohort, 0.881 (95% CI: 0.848–0.914) in the internal validation cohort, and 0.875 (95% CI: 0.842–0.908) in the external validation cohort, significantly outperforming the conventional clinical model (AUC: 0.681, p < 0.001) and single‐dimensional models (genetic model: AUC = 0.723; serum factor model: AUC = 0.756; imaging model: AUC = 0.782). DCA confirmed that the multidimensional model provided a higher net clinical benefit across a wide range of threshold probabilities. Stratification analysis revealed that patients with wild‐type BDNF Val66Met genotype, high serum BDNF/NGF levels, and preserved white matter tract integrity had an 88.3% probability of favorable spontaneous recovery. Conclusion The multidimensional predictive model developed in this study demonstrates excellent performance in predicting 3‐month neurological outcomes after ICH, which can effectively stratify patients into spontaneous recovery and intensive rehabilitation‐required subgroups, providing a reliable basis for personalized post‐ICH rehabilitation management.