Artificial intelligence in Acute Stroke Imaging: Hype or Clinical Reality?

The use of Artificial Intelligence (AI), especially deep learning, in acute stroke imaging is growing. Large vascular occlusion (LVO) detection, ischemic core assessment, and haemorrhage detection are among the applications of non-contrast CT (NCCT), CT angiography (CTA), and CT perfusion (CTP). Concerns about generalizability, bias, and external validation persist despite research showing positive improvements to workflow and diagnostic performance.

Research indicates that AI has shown good diagnostic accuracy for detecting cerebral haemorrhage on NCCT. Sensitivity was 90.6%, and specificity was 93.9% in a 2025 meta-analysis of 24 trials with 28,441 scans (2). Furthermore, deep learning methods significantly reduced interpretation time and achieved high agreement with expert consensus for ASPECTS assessment (kappa = 0.90) (3). On CTA, AI-assisted LVO detection has demonstrated remarkable diagnostic results. AUC values of 0.96 for M1 occlusions and 0.89 for distal M2 occlusions were noted in a prospective multicenter study of 1,052 CTA exams; however, 12% of cases had false-positive results (4). Faster identification of patients qualified for thrombectomy may be made possible by these technologies. With a mean absolute error of 11.22 mL in relation to the ultimate infarct amount on follow-up MRI, AI outperformed human readers in the estimation of ischemic core volume (5). However, people with atrial fibrillation or a history of infarction performed poorly, and in 18% of cases, core volume was overestimated by more than 15 mL (5).

AI has various advantages, including increased consistency, faster image interpretation, and shorter treatment times, all of which result in cost reductions (6, 8, 9). It may also help to discover minor ischemic alterations that less experienced readers frequently overlook (10).

However, there are considerable limitations. Performance can fall significantly across different scanners, imaging techniques, and patient groups, with one study revealing a sensitivity drop from 91.2% to 59.8% after external validation (7).

Whether AI represents hype or clinical reality depends on the application. AI has become a practical reality for haemorrhage detection and LVO triage, improving workflow efficiency and enabling quick treatment decisions. AI, on the other hand, remains an adjunct to expert judgment in ASPECTS grading and infarct core calculation, particularly in atypical cases with suboptimal imaging. Importantly, a comprehensive evaluation of 72 AI stroke imaging studies found that only 23% were free of bias; the main problems included selection bias (29%), confounding (38%), and a lack of external validation (8%). (10). To overcome methodological inadequacies, AI should be viewed as a supplement to clinical decision-making, rather than a replacement for it.

In conclusion, AI has demonstrated meaningful progress in acute stroke imaging. Clinical and prospective studies demonstrate quantifiable advantages for particular activities, such as ASPECTS scoring, LVO detection, and haemorrhage identification. However, it appears that AI is not yet a widespread, unsupervised clinical reality due to persistent issues with generalizability and a lack of rigorous external validation. Requiring external validation before local implementation, ongoing performance monitoring, and maintaining the radiologist and stroke physician as the ultimate arbiters are all necessary steps in the right direction.

References:

1. Azzam (2025). Human Readers versus AI-Based Systems in ASPECTS Scoring for Acute Ischemic Stroke: A Systematic Review and Meta-Analysis with Region-Specific Guidance. ASIDE Intern Med.1(4):1-9.
2. Kamel (2026). Artificial intelligence in stroke imaging. Semin Neurol. 46(01):86-91.
3. Ayobi A, Davis A, Chang PD, Chow DS, Nael K, Tassy M, Quenet S, Fogola S, Shabe P, Fussell D, Avare C, Chaibi (2025). Deep-learning-based ASPECTS algorithm enhances reader performance and reduces interpretation time. 46(3):544-551
4. Sooranahalli Nabh Y, Rayapati AD, Hamid (2025). Artificial intelligence in acute neuroimaging pathways. Cureus;17(12)
5. Lee (2025). Deep learning for ischaemic core estimation on CT perfusion. Stroke. 2025;56(7):1789-1798.
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7. Gete KY, Ayele (2025). AI for ischaemic stroke detection on non-contrast CT. 25:507.
8. Doheim M, Starr M, Bhatt N, Al-Bayati A, Rocha M, Kenmuir C, Romero C, Henry S, Nogueira (2025) Evaluating an AI-driven stroke triage platform. Stroke Vasc Interv Neurol. 2;5(Suppl 1)
9. Noseworthy PA, Attia ZI, Brewer LC, Hayes SN, Yao X, Kapa S, Friedman PA, Lopez-Jimenez (2020). Racial and ethnic bias in deep learning models for stroke detection. J Am Med Inform Assoc. 32(4):789-797.
10. Park (2025) Rethinking clinical AI applications in stroke. J Neurosonol Neuroimag. 17(1):1-10