Enterprise AI Analysis

Unified multimodal learning for stroke triage: joint detection, scoring, and segmentation of acute ischemic stroke

Timely and accurate triage of acute ischemic stroke patients remains a critical challenge in clinical practice, particularly across heterogeneous imaging protocols and multi-center settings. Existing approaches often focus on isolated tasks such as large-vessel occlusion detection, collateral scoring, or infarct segmentation, leading to fragmented pipelines with limited generalizability and increased latency. In this work, we introduce a unified multimodal framework that jointly integrates non-contrast CT, CT angiography, and CT perfusion under a single encoder-decoder architecture. The system employs symmetry-aware encoders, graph-based vascular modeling, and spatio-temporal fusion, followed by multi-task heads for detection, grading, and segmentation. Trained and validated on multi-center datasets, the framework demonstrates consistent gains over state-of-the-art baselines: AUC improvements of up to +0.05 for LVO detection, a +0.11 increase in quadratic-weighted K for collateral scoring, and a +5% Dice improvement with reduced volume error for infarct delineation. Beyond technical metrics, the model achieves clinically relevant impact by reducing mis-triage and unnecessary transfers while lowering inference time to under one minute in real-world PACS deployment. Ablation studies further confirm the synergistic benefit of multi-task optimization, which narrows the generalization gap and enhances robustness across unseen centers. Taken together, these results high-light the potential of unified multimodal learning to support fast, reliable, and scalable decision-making in acute stroke care.