Machine Learning for Distal Medium-Vessel Occlusion Detection: Advances, Challenges, and Future Directions

Distal medium-vessel occlusions (DMVOs) account for roughly 25%-40% of acute ischemic strokes and often evade early detection, delaying treatment, and worsening outcomes. Conventional imaging (non-contrast CT, CT angiography [CTA], MR angiography [MRA]) can miss smaller distal thrombi, and even experienced readers have limited sensitivity, which can be as low as 35%. Recent studies highlight that advanced neuroimaging (CT perfusion, multiphase CTA, magnetic resonance imaging [MRI]) and automated analysis improve DMVO identification. In particular, machine learning (ML) and deep learning algorithms have shown promise in detecting subtle occlusions on multimodal stroke imaging. This review summarizes current imaging approaches for DMVOs, surveys ML-based detection methods, and examines validation studies and clinical evidence. We discuss barriers to clinical integration, including the need for large, annotated datasets and regulatory validation. Finally, we outline future directions: improved algorithms (explainable AI, multimodal networks), prospective trials, and workflow integration in the neurovascular service. In sum, ML-driven DMVO detection holds potential to augment rapid stroke care, but further research and collaboration are needed to translate these tools into routine practice.