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Deep learning has become increasingly prominent in stroke imaging; however, its clinical role differs substantially between computed tomography (CT) and magnetic resonance imaging (MRI).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00234-026-04095-5","type":"journal-article","created":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T06:43:29Z","timestamp":1782456209000},"page":"3009-3040","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Deep learning in acute ischemic stroke imaging: a systematic review of CT- and MRI-based segmentation, triage, and prognostic modeling"],"prefix":"10.1007","volume":"68","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4270-2153","authenticated-orcid":false,"given":"Ismail","family":"Kunduracioglu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suat","family":"Ince","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bilal","family":"Bayram","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Serhat","family":"Kilicarslan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erkan","family":"Veziroglu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ishak","family":"Pacal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,26]]},"reference":[{"key":"4095_CR1","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1161\/CIRCRESAHA.116.308413","volume":"120","author":"VL Feigin","year":"2017","unstructured":"Feigin VL, Norrving B, Mensah GA (2017) Global Burden of Stroke. 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