Machine Learning for Distal Medium-Vessel Occlusion Detection: Advances, Challenges, and Future Directions.
Authors
Affiliations (14)
Affiliations (14)
- Staten Island University Hospital, Staten Island, New York, New York, USA.
- Touro College of Osteopathic Medicine, New York, New York, USA.
- Department of Radiology, Division of Neuroradiology, Johns Hopkins Hospital, Baltimore, Maryland, USA.
- Department of Neuroradiology, The University of Texas MD Anderson Medical Center, Houston, Texas, USA.
- Department of Neuroradiology, Rockefeller Neuroscience Institute, West Virginia University, Morgantown, West Virginia, USA.
- Department of Neurology, University of Maryland Medical Center, Baltimore, Maryland, USA.
- Renaissance School of Medicine, Stony Brook University, Stony Brook, New York, New York, USA.
- Division of Radiology, The Queen's Medical Center, University of Hawaii, Honolulu, Hawaii, USA.
- Neuroendovascular Program, Massachusetts General Hospital, Harvard University, Boston, Massachusetts, USA.
- Departments of Medical Imaging and Neurosurgery, Neurovascular Centre, St. Michael's Hospital, Toronto, Ontario, Canada.
- Department of Diagnostic and Interventional Neuroradiology, Erasme University Hospital, Brussels, Belgium.
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.
- Department of Interventional Neuroradiology, Stanford University Medical Center, Palo Alto, California, USA.
- Department of Radiology, Neuroendovascular Program, University Medical Center, Muenster, Germany.
Abstract
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.