The multimodal deep learning model achieved a mean AUROC of 0.79 for ischemic stroke prediction, outperforming unimodal models which had AUROCs of 0.72 and 0.69.
Does a multimodal deep learning model integrating atrial phenotypic and genotypic data improve ischemic stroke prediction compared to unimodal models in people from the UK Biobank?
Integrating atrial phenotypic and genotypic data into a multimodal deep learning model significantly improves the prediction of ischemic stroke compared to using clinical or demographic data alone.
Introduction: Better methods to predict ischemic stroke (IS) would improve stroke prevention. The role of atrial abnormalities other than atrial fibrillation (AF) in causing strokes is uncertain. We developed unimodal and multimodal deep learning models that include atrial traits to improve understanding of their contribution to stroke risk and to develop more accurate ways to predict stroke. Methods: We studied 24,570 people from the UK Biobank for whom cardiac MRI data and EKG data are available; of these, 100 had IS. We built multimodal multi-layer perceptron with late fusion (MMLP-LF) models to predict IS by integrating 5 data modalities: 1) MRI and EKG atrial traits, 2) lead genetic variants (P<5e-8) from GWAS of atrial traits, 3) patient demographics, 4) clinical diagnoses and 5) polygenic risk scores (PRS) for cardiovascular risk factors and other diseases. We compared the performance of models incorporating different modalities using 10 rounds of repeated random sampling validation. In each round, we split the samples at 64%–16%–20% for training, validation, and test sets. Models were trained with 20 rounds of random initialization. The validation set was used for model selection. We used Area Under the Receiver Operating Characteristic Curve (AUROC) to assess models. We performed Shapley additive explanation (SHAP) analysis to evaluate contributions of individual features. Results: Our MMLP-LF model including all 5 modalities achieved a mean AUROC of 0.79 ± 0.06 for predicting IS on the test set, substantially outperforming the best unimodal models which were based on clinical attributes (AUROC 0.72 ± 0.10) or demographic variables only (AUROC 0.69 ± 0.06). SHAP analysis across 10 random splits revealed that hypertension and hyperlipidemia are consistently the two most influential contributors to the full model with 5 modalities. The patient’s age at the time of cardiac imaging and left atrial maximum volume (LAVmax) were in the top 10 contributors in 9 of the 10 rounds, and CAD, diabetes, and PQ-interval in 7 of the 10 rounds. Conclusion: Our MMLP-LF model improved IS prediction over unimodal models and identified clinical, demographic, phenotypic and genotypic drivers predicting IS. While classical risk factors are the main contributors to the model, atrial parameters such as LAVmax and PQ-interval contribute significantly to the models, suggesting the potential importance of atrial abnormalities other than AF in increasing stroke risk.
Leifer et al. (2026) studied this question. The multimodal deep learning model achieved a mean AUROC of 0.79 for ischemic stroke prediction, outperforming unimodal models which had AUROCs of 0.72 and 0.69.
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