aceArtificial Intelligence in Ischemic Stroke Lesion Segmentation: A Narrative Review of Deep Learning Methods, Clinical Utility, and Future Directions
Ischemic stroke management is time-sensitive, and lesion segmentation supports treatment selection, prognostication, and reproducible quantification. Deep learning (DL) aims to accelerate and standardize lesion delineation to augment neuroimaging workflows. We conducted a narrative review of DL-based ischemic stroke lesion segmentation studies published from 2020 to 2025. PubMed, Google Scholar, Scopus, and IEEE Xplore were searched; ~ 500 records were identified, and 40 full-text studies were included after scr…
Deep learning models, especially U-Net variants and newer transformer ensembles, improved ischemic stroke lesion segmentation on MRI, achieving Dice scores above 0.80 and approaching 0.90 in multisite DWI reports, to support faster treatment selection and quantification.
Most studies were retrospective and may not reflect real-world performance and access constraints, and CT segmentation remains constrained by subtle early ischemic changes and poor generalization, limiting equitable clinical deployment.
Most included studies were retrospective and may not reflect real-world performance, access constraints, and generalization, particularly for CT where subtle early ischemic changes limit reliability.
Evidence
- Peer-reviewedJournal of Imaging Informatics in Medicine2026-07-17
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Truvace Impact Record TRV-2026-0268, v1: “Artificial Intelligence in Ischemic Stroke Lesion Segmentation: A Narrative Review of Deep Learning Methods, Clinical Utility, and Future Directions.” Truvace, 2026-07-19. /record/TRV-2026-0268 (accessed at citation time). sha256 8cbadb5a09acb79e…
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