• Fadi Al Saiegh, Alfredo Munoz, Lohit Velagapudi, Thana Theofanis, Neil Suryadevara, Priyadarshee Patel, Roland Jabre, Ching-Jen Chen, Mohamed Shehabeldin, Michael Reid Gooch, Pascal Jabbour, Stavropoula Tjoumakaris, Robert H Rosenwasser, and Nabeel A Herial.
• Department of Neurosurgery, Thomas Jefferson University, Philadelphia, Pennsylvania, USA.
• J Neuroimaging. 2022 Sep 1; 32 (5): 798807798-807.

AbstractMechanical thrombectomy (MT) for ischemic stroke due to large vessel occlusion is standard of care. Evidence-based guidelines on eligibility for MT have been outlined and evidence to extend the treatment benefit to more patients, particularly those at the extreme ends of a stroke clinical severity spectrum, is currently awaited. As patient selection continues to be explored, there is growing focus on procedure selection including the tools and techniques of thrombectomy and associated outcomes. Artificial intelligence (AI) has been instrumental in the area of patient selection for MT with a role in diagnosis and delivery of acute stroke care. Machine learning algorithms have been developed to detect cerebral ischemia and early infarct core, presence of large vessel occlusion, and perfusion deficit in acute ischemic stroke. Several available deep learning AI applications provide ready visualization and interpretation of cervical and cerebral arteries. Further enhancement of AI techniques to potentially include automated vessel probe tools in suspected large vessel occlusions is proposed. Value of AI may be extended to assist in procedure selection including both the tools and technique of thrombectomy. Delivering personalized medicine is the wave of the future and tailoring the MT treatment to a stroke patient is in line with this trend.© 2022 American Society of Neuroimaging.

15 keywords...

hide…