  Stroke is a common disabling neurological condition that affects about
one-quarter of the adult population over age 25; more than half of patients
still have poor outcomes, such as permanent functional dependence or even
death, after the onset of acute stroke. The aim of this study is to investigate
the efficacy of diffusion-weighted MRI modalities combining with structured
health profile on predicting the functional outcome to facilitate early
intervention. A deep fusion learning network is proposed with two-stage
training: the first stage focuses on cross-modality representation learning and
the second stage on classification. Supervised contrastive learning is
exploited to learn discriminative features that separate the two classes of
patients from embeddings of individual modalities and from the fused multimodal
embedding. The network takes as the input DWI and ADC images, and structured
health profile data. The outcome is the prediction of the patient needing
long-term care at 3 months after the onset of stroke. Trained and evaluated
with a dataset of 3297 patients, our proposed fusion model achieves 0.87, 0.80
and 80.45% for AUC, F1-score and accuracy, respectively, outperforming existing
models that consolidate both imaging and structured data in the medical domain.
If trained with comprehensive clinical variables, including NIHSS and
comorbidities, the gain from images on making accurate prediction is not
considered substantial, but significant. However, diffusion-weighted MRI can
replace NIHSS to achieve comparable level of accuracy combining with other
readily available clinical variables for better generalization.
