Hybrid CNN-ViT Modeling For Predicting Functional Outcome After Ischemic Stroke: A Retrospective Study

Summary

A hybrid CNN-ViT model extracted multiparametric MRI features, and stacked logistic regression fused imaging and clinical predictions to estimate 90-day functional outcome after acute ischemic stroke. The fusion model achieved the highest internal test performance, with an AUC of 0.885 and accuracy of 0.840, and showed encouraging external validation performance.

Abstract

Acute ischemic stroke (AIS) is a major cause of death and long-term disability, and early prediction of functional recovery is important for guiding clinical decision-making and rehabilitation planning. Functional outcome at 90 days is commonly assessed using the modified Rankin Scale (mRS), but predicting long-term outcome from early clinical and imaging information remains challenging. We hypothesized that integrating multiparametric magnetic resonance imaging (MRI) with structured clinical variables using a hybrid convolutional neural network (CNN)-Vision Transformer (ViT) architecture would improve prediction of 90-day functional outcomes compared with single-modality models. A retrospective cohort of 300 AIS patients who underwent multiparametric MRI was analyzed and divided into a training-validation cohort (n = 250) and an internal independent test cohort (n = 50). An additional external test cohort of 37 AIS patients was included to assess model generalizability. A hybrid CNN-ViT model was developed to extract multiparametric MRI features, and imaging and clinical predictions were integrated using stacked logistic regression. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Among the evaluated clinical models, the support vector machine achieved the highest internal test AUC (0.878). The imaging model achieved an AUC of 0.782. The multimodal fusion model achieved the best overall internal performance, with an AUC of 0.885, sensitivity of 0.920, specificity of 0.760, and accuracy of 0.840. Similar performance trends were observed in the external test cohort. These findings suggest that stacked fusion of multiparametric MRI and clinical predictions may improve 90-day functional outcome prediction after AIS. However, larger multicenter validation studies are required before clinical implementation.

Introduction

Acute ischemic stroke (AIS) remains a leading cause of death and long-term disability, and clinicians must estimate recovery early when treatment and discharge decisions are most time-sensitive¹. Accurate early prediction of functional recovery is therefore clinically important for guiding treatment strategies, rehabilitation planning, and discharge decision-making. Ninety-day functional outcome is a standard endpoint in stroke research and care and is most commonly measured using the modified Rankin Scale (mRS), which has established validity and reliability in clinical trials and observational studies^2,

Protocol

This study was approved by the Institutional Review Board of Shenzhen University General Hospital (IRB approval no. KYLL-2026-077-1). The requirement for written informed consent was waived due to the study's retrospective design.

1. Study cohort

Patients with acute ischemic stroke (AIS) who underwent multiparametric MRI were retrospectively identified from the institutional clinical database. The overall study workflow, including dataset partitioning, imaging model development, clinical model construction, and multimodal fusion, is illustrated in Figure 1.

Results

Study cohort

A total of 300 patients with acute ischemic stroke (AIS) were included after application of predefined inclusion and exclusion criteria. The dataset was divided into a training-validation cohort (n = 250) and an internal independent test cohort (n = 50) using stratified sampling based on functional outcome distribution. In addition, an external test cohort of 37 AIS patients from an independent dataset was included to provide preliminary external validation. Strat.......

Discussion

This study developed and evaluated a multimodal artificial intelligence framework integrating multiparametric MRI and structured clinical variables for prediction of 90-day functional outcome after acute ischemic stroke (AIS). The proposed fusion model demonstrated higher predictive performance than either the imaging or clinical model alone. Specifically, the stacked multimodal model integrating predictions from the support vector machine (SVM)-based clinical model and the imaging model achieved an AUC of 0.885 in the i.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Magnetic resonance imaging system ((Discovery MR750 3.0T)	GE Healthcare	https://www.gehealthcare.in/products/magnetic-resonance-imaging/3-0t/discovery-mr750	Clinical MRI scanner used for acquisition of DWI, ADC, and T2-FLAIR images
Phased-array head coil	GE Healthcare	https://services.gehealthcare.com/gehcstorefront/p/5450630	Head coil used for MRI signal reception
Graphics processing unit (RTX 4090)	NVIDIA	https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4090/	GPU used for deep learning model training
Deep learning framework (PyTorch v2.1)	PyTorch Foundation	https://pytorch.org/	Framework used to implement the CNN-Transformer model
Programming language (Python v3.10)	Python Software Foundation	https://www.python.org/downloads/release/python-3100/	Environment used for model development and data processing
Machine learning library (scikit-learn)	scikit-learn Developers	https://scikit-learn.org/stable/	Used for clinical machine learning models and statistical analysis
Gradient boosting library (XGBoost)	DMLC	https://xgboost.readthedocs.io/en/release_3.2.0/	Used for gradient boosting model implementation
Gradient boosting library (LightGBM)	Microsoft	https://lightgbm.readthedocs.io/en/stable/	Used for LightGBM clinical model