Frontiers in neurology · 2026
A multimodal deep learning model for predicting early neurological deterioration in patients with acute ischemic stroke.
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Sun Chenglin, Zhang Yaqiong, Zhou Peiyang, Lu Zuneng
Аннотация
INTRODUCTION: Timely identification of patients at high risk for early neurological deterioration (END) is critical for effective intervention after acute ischemic stroke; however, existing prediction models largely rely on structured clinical data and underutilize semantic information from imaging findings.
PATIENTS AND METHODS: In this retrospective single-center study at Xiangyang No.1 People's Hospital (January 2018-December 2023), 426 patients with acute ischemic stroke and imaging-confirmed middle cerebral artery occlusion who received non-endovascular treatment were included. Patients with other arterial occlusions, endovascular therapy, or incomplete data were excluded. END was defined as a ≥2-point increase in total National Institutes of Health Stroke Scale (NIHSS) score or a ≥1-point increase in the motor subscore within 7 days after admission. Structured clinical variables and radiology report text collected at admission were integrated into a multimodal deep learning model. Model performance was evaluated using AUC, accuracy, recall, precision, F1-score, calibration, and decision curve analysis, with interpretability assessed using SHAP and Integrated Gradients.
RESULTS: Among all patients, 38.0% exhibited END (30.3% early; 7.7% late). The multimodal Concat-Fusion model achieved AUCs of 0.877 (training) and 0.771 (test), surpassing single-modality models, with strong predictive capabilities for early (AUC = 0.842) and late END (AUC = 0.855). Subgroup analyses confirmed consistent performance across NIHSS scores, D-dimer levels, hypertension, and atrial fibrillation, with significantly higher AUC in diabetic patients (p = 0.002). SHAP analysis identified D-dimer, diastolic blood pressure, and heart rate as key contributors. Integrated Gradients revealed symptom descriptors including "unsteadiness," "walking," and "vision" as significant textual predictors. Risk stratification effectively distinguished high- and low-risk groups with significantly different cumulative END incidences within 48 h and 7 days (log-rank p < 0.0001). High-risk patients exhibited poorer NIHSS trajectories during 14-day follow-up.
CONCLUSION: This multimodal prediction model may improve early identification of END and support individualized clinical management. Larger prospective studies are required to validate its clinical utility.
PATIENTS AND METHODS: In this retrospective single-center study at Xiangyang No.1 People's Hospital (January 2018-December 2023), 426 patients with acute ischemic stroke and imaging-confirmed middle cerebral artery occlusion who received non-endovascular treatment were included. Patients with other arterial occlusions, endovascular therapy, or incomplete data were excluded. END was defined as a ≥2-point increase in total National Institutes of Health Stroke Scale (NIHSS) score or a ≥1-point increase in the motor subscore within 7 days after admission. Structured clinical variables and radiology report text collected at admission were integrated into a multimodal deep learning model. Model performance was evaluated using AUC, accuracy, recall, precision, F1-score, calibration, and decision curve analysis, with interpretability assessed using SHAP and Integrated Gradients.
RESULTS: Among all patients, 38.0% exhibited END (30.3% early; 7.7% late). The multimodal Concat-Fusion model achieved AUCs of 0.877 (training) and 0.771 (test), surpassing single-modality models, with strong predictive capabilities for early (AUC = 0.842) and late END (AUC = 0.855). Subgroup analyses confirmed consistent performance across NIHSS scores, D-dimer levels, hypertension, and atrial fibrillation, with significantly higher AUC in diabetic patients (p = 0.002). SHAP analysis identified D-dimer, diastolic blood pressure, and heart rate as key contributors. Integrated Gradients revealed symptom descriptors including "unsteadiness," "walking," and "vision" as significant textual predictors. Risk stratification effectively distinguished high- and low-risk groups with significantly different cumulative END incidences within 48 h and 7 days (log-rank p < 0.0001). High-risk patients exhibited poorer NIHSS trajectories during 14-day follow-up.
CONCLUSION: This multimodal prediction model may improve early identification of END and support individualized clinical management. Larger prospective studies are required to validate its clinical utility.