Abstract:
Background
Timely detection of acute ischemic stroke is crucial for ensuring an appropriate standard of care and guiding reperfusion therapies. While MRI remains highly sensitive for identifying early ischemic changes, its interpretation is subject to inter-observer variability and may be less accessible or efficient in resource-limited settings. Emerging AI models demonstrate high specificity, sensitivity, and accuracy in detecting stroke lesions, offering the potential to enhance diagnostic efficiency and streamline clinical workflows in stroke management.
Methods
A systematic review of acute stroke diagnostic-focused AI studies from January 2014 to September 2025 in PubMed, MEDLINE, CENTRAL, Google Scholar, and Cochrane Library using terms: ’artificial intelligence’ or and ’ischemic stroke’ and ‘DWI’ and ‘FLAIR’ was performed. The overall certainty of the evidence was evaluated using the GRADE approach.
Results
AI models demonstrate high accuracy, sensitivity, and specificity in detecting acute ischemic lesions on MRI, particularly in cases of large infarcts, thereby supporting timely decision-making for patients eligible for reperfusion therapies.
Conclusion
AI models enable rapid and accurate identification of stroke lesions, serving as a valuable adjunct in the early detection of ischemic changes.
Biography:
Dr. Shari Guerra is currently a Neurology resident in The Medical City – Ortigas and is a graduate of the University of the East Ramon Magsaysay Memorial Medical Center Inc. College of Medicine in 2017. She earned her Bachelor of Science in Biology, Major in Wildlife Biology in the University of the Philippines Los Baños where she was awarded Best in Undergraduate Thesis Award and Oral Presentation at the 26th Philippine Biodiversity Symposium of the Biodiversity Conservation Society of the Philippines for her topic on the Description and comparison of Philippine hornbill (Bucerotidae) vocalizations.
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