Deep learning methods for the detection of stroke for neuropathology: Lessons learned from <i>in vivo</i> imaging to digital pathology.
Authors
Affiliations (7)
Affiliations (7)
- Department of Electrical and Computer Engineering, University of California Davis, Davis, CA, USA.
- Computer Science and Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, UP, India.
- Department of Biomedical Engineering, University of California Davis, Davis, CA, USA.
- Department of Pathology and Laboratory Medicine, University of California Los Angeles, Los Angeles, CA, USA.
- Department of Internal Medicine, University of California Davis, Davis, CA, USA.
- Electrical and Computer Engineering, University of Kentucky, Lexington, KY, USA.
- Department of Pathology and Laboratory Medicine, University of California Davis, Davis, CA, USA.
Abstract
Stroke is a leading cause of death and a major contributor to long-term disability worldwide. Accurate stroke assessment is critical for both research and clinical care. <i>In vivo</i> neuroimaging modalities, particularly CT and MRI, are widely used and increasingly coupled with deep learning (DL) to improve stroke assessment. These approaches have matured rapidly and benefit from harmonized imaging protocols and growing multi-center datasets, but they remain limited in sensitivity to microinfarcts, subtle early ischemic changes, and cellular-level pathology. Digital pathology, through whole-slide imaging, offers unmatched tissue-level resolution and biological ground truth for validating imaging biomarkers, yet DL applications for infarct analysis in histopathology remain comparatively underdeveloped and data-limited, with most existing work focused on vascular morphometry or neurodegenerative pathology rather than infarct analysis. This review aims to provide insights from pathology, radiology, and computational sciences. We summarize dominant architectural trends in <i>in vivo</i> imaging, review emerging computational pathology approaches, and describe MRI-pathology correlation and <i>ex vivo</i> validation workflows. We further examine key translational challenges, including dataset scarcity, scanner and staining variability, demographic bias, computational feasibility, and limited external validation. Finally, we outline future directions emphasizing harmonized data infrastructure, multimodal integration, and emerging foundation models to enable scalable, biologically grounded, and equitable infarct assessment across clinical and research settings.