# Interpretable Multimodal MRI-Clinical Fusion for Functional Outcome Prediction After Acute Ischemic Stroke

**Authors:** Sina Raeisadigh, Henning Müller, Abderrahmane Hedjoudje  
**Lab:** University of Geneva · **Published:** 5 Nov 2026  
**Links:** [arXiv](https://arxiv.org/abs/2611.08154) · [PDF](https://arxiv.org/pdf/2611.08154) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.08154.html)

## Abstract

This study evaluates the added prognostic value of day-1 multimodal MRI for predicting three-month functional outcome after acute ischemic stroke (AIS). Sixty-six AIS patients with ADC, DWI, FLAIR, and clinical data were analyzed. Three-dimensional pretrained ResNet embeddings were extracted from each MRI modality, reduced using fold-isolated principal component analysis, and combined with structured clinical variables and ADC-derived lesion biomarkers. Linear support vector machines were evaluated using eight-fold stratified group cross-validation across MRI modality combinations. Clinical-only modeling achieved strong performance (AUC = 0.899 ± 0.151), while MRI-only models showed limited discrimination (AUC ≈ $0.67$-$0.73$). Among multimodal configurations, FLAIR-clinical fusion achieved the highest mean cross-validated performance (AUC = 0.948 ± 0.061), followed by ADC-clinical fusion (AUC = 0.941 ± 0.094). Full ADC-DWI-FLAIR fusion did not consistently outperform single-modality clinical fusion. Feature-importance analysis identified age, NIHSS subscores, ADC lesion burden, and learned FLAIR representations as key contributors, while qualitative FLAIR activation maps showed responses over lesion-like regions. These findings suggest that compact day-1 FLAIR and ADC representations provide complementary prognostic information beyond clinical variables and support interpretable, data-efficient outcome prediction in AIS.

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