Stroke diagnosis improved by AI support, multinational study finds

by Korea University College of Medicine

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Dr. Chi Kyung (CK) Kim of the Department of Neurology at Korea University Guro Hospital and his research team successfully conducted a multinational validation of AI technology that precisely detects large vessel occlusion (LVO) in acute stroke patients using only standard brain CT (non-contrast CT) without contrast agents. The findings are published in the Journal of NeuroInterventional Surgery. The team includes research Dr. Kim Beom-joon of the Department of Neurology, Dr. Jun Sun-woo of the Department of Radiology at Seoul National University Bundang Hospital, and CMO Ryu Wi-sun of JLK.

Acute ischemic stroke (cerebral infarction) is caused by blocked blood vessels in the brain. Endovascular thrombectomy is performed to open the blockage and is crucial to a patient's survival and prognosis. Therefore, CT angiography with a contrast agent is necessary, but such procedures are often delayed or impossible because of the emergency room environment or hospital system.

There has been a persistent need for technology that can rapidly identify large vessel occlusion using only non-contrast CT, a basic examination method.

To evaluate the diagnostic performance and clinical utility of JLK's non-contrast CT-based large vessel occlusion identification AI algorithm, the research team analyzed multinational cohort data from 963 patients, including 723 Korean patients and 240 U.S. patients.

The results showed that the area under the curve (AUC) representing the AI's diagnostic performance for large vessel occlusion was 0.963 for the Korean patient group and 0.899 for the U.S. patient group. The study also confirmed its usability in different clinical settings by maintaining high diagnostic performance with patient data from different countries and various CT scanning equipment.

The effect of AI support was also evident in a cross-validation study involving eight medical professionals, including specialists and residents. When medical professionals read non-contrast CT scans without AI, the diagnostic accuracy was 0.718 AUC. With AI assistance, it improved to 0.852 AUC. The sensitivity of accurately identifying patients with large vessel occlusion increased from 46.6% to 63.7%, and the specificity of distinguishing patients without large vessel occlusion increased from 91.9% to 94.9%.

The research team explained that the results mean that for every 18 non-contrast CT scans read by medical staff, they can identify one additional patient with large vessel occlusion who could have been missed with conventional methods with the help of AI.

Additionally, no clear evidence of automation bias—the tendency of clinicians to uncritically accept AI-generated results—was observed. The research confirmed that AI can serve as a safety net to complement clinicians' judgment during emergency stroke care.

Chi Kyung Kim explained, "This is clinically very significant: We can now rapidly and accurately identify severe stroke patients in the time-sensitive emergency room using only the most accessible equipment: non-contrast CT."

Kim Beom-joon emphasized, "This research is meaningful because it statistically proved that AI can successfully support medical staff's judgment and serve as a safety net to compensate for human errors without 'automation bias' that can distort clinical judgment."

Jun predicted, "With the help of AI, inexperienced medical staff or resident doctors can achieve diagnostic accuracy close to that of skilled specialists. This will significantly raise the standard of care at local hospitals and during nighttime emergencies."

Publication details

Leonard Sunwoo et al, AI assisted detection of large vessel occlusion on non-contrast CT: multinational validation and reader study, Journal of NeuroInterventional Surgery (2026). DOI: 10.1136/jnis-2026-025339

Journal information: Journal of NeuroInterventional Surgery

Key medical concepts

Diagnostic AccuracyAcute Ischemic Strokes

Clinical categories

NeurologyDiagnostic radiology Provided by Korea University College of Medicine Who's behind this story?

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