Predicting Cognitive Decline in Stroke Patients Using Deep Learning

Cognitive decline is a common outcome after stroke, often diminishing survivors’ quality of life. While early detection of post-stroke cognitive impairment (PSCI) is crucial for intervention, conventional diagnostic methods are time-consuming and resource-intensive.

We retrospectively analyzed data from 1,500 stroke patients, of whom 450 (30%) developed cognitive impairment within six months. A hybrid CNN-LSTM model was used to extract spatial and temporal features from MRI data. Model performance was compared with Random Forest and XGBoost, and feature importance was assessed using SHAP.

The CNN-LSTM model achieved an accuracy of 88.5% and an AUC of 0.92, outperforming Random Forest (AUC: 0.85) and XGBoost (AUC: 0.87). Key predictors included NIHSS score, age, white matter hyperintensities, and hippocampal atrophy. Multimodal data integration enhanced predictive performance.

These findings confirm the effectiveness of deep learning models in predicting cognitive decline by integrating imaging and clinical data. The model’s ability to identify structural brain changes and key clinical variables offers potential utility for early detection. However, further validation in prospective cohorts is needed to establish generalizability.

The proposed deep learning model accurately predicts cognitive decline after stroke using multimodal inputs. This approach may assist in early risk stratification and individualized care planning. Further validation in prospective, multicenter studies is warranted.