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Pouya Tahsili-Fahadan: New Machine Learning Model Enhances Risk Prediction in Acute Ischemic Stroke
Pouya Tahsili-Fahadan/LinkedIn
Jul 10, 2026, 12:36

Pouya Tahsili-Fahadan: New Machine Learning Model Enhances Risk Prediction in Acute Ischemic Stroke

Pouya Tahsili-Fahadan, Medical Director of Stroke at Inova Fairfax Hospital, shared on LinkedIn about a recent article he and his colleagues co-authored, published in Translational Stroke Research, adding:

”I’m pleased to share our latest publication in Translational Stroke Research.

In this study, we developed and validated machine learning models that integrate automated quantitative cerebrovascular morphology, collateral assessment, and conventional clinical and imaging variables to improve prediction of major complications following endovascular thrombectomy in acute ischemic stroke.

This work represents a multidisciplinary collaboration between the Inova Fairfax Hospital (Inova Neuroscience and Spine Institute) and the University of California, Riverside.

Congratulations and sincere thanks to all of my co-authors and collaborators for their outstanding work.”

Title: Quantitative Cerebrovascular Analysis for Improved Prediction of Post-Stroke Complications

Authors: Aditi Deshpande, Jing Wang, Laith R. Altaweel, Seajin Yi, Zelalem Bahiru, Tahddeus J. Leiphart, Pouya Tahsili-Fahadan, Kaveh Laksari

Pouya Tahsili-Fahadan: New Machine Learning Model Enhances Risk Prediction in Acute Ischemic Stroke

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