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Deep Learning Applications in Imaging of Acute Ischemic Stroke: A Systematic Review and Narrative Summary
Bin Jiang1, Nancy Pham1, Eric K van Staalduinen1
1Department of Radiology, Stanford University School of Medicine, 1201 Welch Rd, Stanford, CA 94305.
Deep learning enhances acute ischemic stroke (AIS) imaging, excelling in lesion segmentation. Further research needs standardized data and real-world validation for AI in stroke care.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Neurology
Background:
- Acute ischemic stroke (AIS) poses significant health risks, necessitating rapid neuroimaging analysis.
- Deep learning (DL) models show promise in advancing stroke imaging interpretation.
Conclusions:
- DL is significantly impacting AIS imaging, particularly for lesion segmentation.
- Key challenges include standardizing protocols, creating larger public datasets, and validating performance in clinical settings.
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