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Updated: Jun 9, 2026

A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

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Artificial intelligence-assisted detection of challenging ischemic stroke on diffusion-weighted imaging: a reader

Younbeom Jeong¹, Wi-Sun Ryu², Beom Joon Kim³

¹Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea.

Frontiers in Neurology

|June 8, 2026

Summary

Artificial intelligence (AI) significantly enhances the detection of acute ischemic stroke (AIS) on MRI scans. AI assistance improves diagnostic accuracy and lesion segmentation, aiding clinicians in identifying challenging stroke cases.

Keywords:

AI assisted diagnosis artificial intelligence diffusion magnetic resonance imaging ischemic stroke radiologists reader study

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Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

Published on: August 14, 2019

Area of Science:

Radiology and Medical Imaging
Artificial Intelligence in Healthcare
Neurology

Background:

Diffusion-weighted imaging (DWI) is crucial for diagnosing acute ischemic stroke (AIS).
Detecting small and hyperacute AIS lesions on DWI can be challenging for human readers.
Artificial intelligence (AI) offers potential for improving diagnostic accuracy in medical imaging.

Purpose of the Study:

To evaluate the impact of AI assistance on the diagnostic performance of human readers for detecting challenging AIS lesions on DWI.
To assess AI's effectiveness in improving sensitivity, specificity, and lesion segmentation accuracy.

Main Methods:

A retrospective, single-center, randomized crossover study.
Inclusion of 250 cases (130 AIS, 120 controls) from a cohort of 3,986 patients.
Five readers interpreted DWI cases with and without AI assistance, evaluating diagnostic performance metrics.

Main Results:

AI-assisted reading significantly improved the area under the receiver operating characteristic curve (AUC) from 0.85 to 0.93 (p < 0.01).
Pooled sensitivity increased from 74.6% to 90.6% (p < 0.01), and lesion segmentation accuracy (Dice Similarity Coefficient) improved from 0.523 to 0.742 (p < 0.01).
Specificity slightly decreased to 84.0% (p = 0.05), while reader confidence notably improved with AI support.

Conclusions:

AI assistance significantly enhances diagnostic performance for detecting small and hyperacute AIS lesions on DWI.
AI improves lesion segmentation accuracy and reader confidence, particularly in challenging cases.
AI tools show promise in augmenting radiologists' capabilities in acute stroke diagnosis.