Accurate prediction of long-term functional outcomes for stroke patients remains a clinical challenge, despite advances in diagnostics and treatments. Most machine learning (ML) and artificial intelligence (AI) outcome prediction models lack robust strategies to incorporate high-dimensional clinical imaging data or to account for data availability at the point of care. In this study, we present an enhanced representation learning pipeline that fuses 2.5D magnetic resonance images (MRI), clinical data, and imaging biomarkers to predict 90-day modified Rankin Scale (mRS) for ischemic stroke patients. Using autoencoder-generated MRI embeddings, we systematically evaluate multiple ML/AI methods for both classification (90-day mRS > 2) and ordinal regression. Critically, we incorporate a privileged information paradigm, leveraging features available during training to enhance model generalizability without requiring them during inference. We developed and validated our models on a large public dataset (N = 974) and an external validation dataset (N = 738), demonstrating performance comparable to state-of-the-art convolutional neural network-based approaches with the added benefit of modularity (Test AUC 0.801; F1 0.699; MAE 1.179). Our results highlight the promise of representation learning and privileged information paradigms for bridging the gap between research and bedside prognosis in computational neurology.
Wittrup et al. (Sat,) studied this question.
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