Deep learning in acute ischemic stroke imaging: a systematic review of CT- and MRI-based segmentation, triage, and prognostic modeling.

BACKGROUND: Acute ischemic stroke is a time-critical neurological emergency in which imaging directly influences diagnosis, treatment eligibility, tissue-at-risk estimation, workflow prioritization, and outcome prediction. Deep learning has become increasingly prominent in stroke imaging; however, its clinical role differs substantially between computed tomography (CT) and magnetic resonance imaging (MRI). OBJECTIVE: This PRISMA-guided systematic review evaluated peer-reviewed studies published between January 2023 and March 2026 that applied deep learning to acute ischemic stroke imaging. METHODS: Searches across major biomedical, engineering, and clinical databases identified 91 eligible studies. Extracted data included imaging modality, dataset source, clinical task, model architecture, validation strategy, and reported performance metrics. RESULTS: MRI-based studies predominantly addressed lesion segmentation and tissue characterization using DWI, ADC, FLAIR, and multimodal MRI. In this setting, U-Net-derived architectures, self-configuring frameworks, attention-based models, and selected CNN-Transformer hybrids frequently achieved strong Dice and IoU values on benchmark datasets, particularly ISLES 2015 and ISLES 2022. CT-based studies followed a different clinical direction, with greater emphasis on emergency triage, hemorrhage exclusion, large-vessel occlusion detection, stroke classification, ASPECTS-related assessment, and CTP-based core-penumbra estimation. High retrospective accuracy or AUC values in CT studies should be interpreted cautiously, since image-level classification performance does not establish reliable voxel-level lesion delineation, especially on NCCT, where early ischemic changes remain subtle and difficult to segment. CONCLUSION: Across both modalities, technical progress remains ahead of clinical validation. External testing, patient-level separation, standardized reporting, scanner robustness, interpretability, and prospective workflow evaluation are still inconsistently addressed. The field now requires a shift from isolated benchmark performance toward clinically interpretable, externally validated, and prognosis-aware systems that can support real-time neuroradiology decision-making.