ORIGINAL RESEARCH article
Front. Stroke
Sec. Acute Stroke and Interventional Therapies
Volume 4 - 2025 | doi: 10.3389/fstro.2025.1610399
DURING MECHANICAL THROMBECTOMY FOR THE TREATMENT OF ACUTE ISCHEMIC STROKE
Provisionally accepted- 1Medtronic Neurovascular, Irvine, CA, United States
- 2Princeton University, Princeton, New Jersey, United States
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Background: Mechanical thrombectomy is the standard of care for acute ischemic stroke caused by proximal large-vessel occlusion in the anterior circulation. In the stent retriever approach, a nitinol stent engages the clot via outward radial force to enable removal. However, current procedures lack direct clot visualization under fluoroscopy, which can reduce retrieval efficacy and often require multiple passes.Improving first-pass success is critical given the time-sensitive nature of stroke intervention.Methods: This study presents a clot visualization method using the spatial arrangement of radio-opaque markers on the Medtronic Solitaire⢠stent. A deep learning model, Clot[U]-Net, based on the U-Net architecture, was trained on 800 anteroposterior and lateral in-vitro images and evaluated on a separate test set.Intersection over Union (IOU) of 87.9% and an AUROC of 89.9%, and standard deviations of 2.2 and 3.16, respectively.The proposed method enables clot visualization during stent retriever thrombectomy without altering existing clinical workflows. With further pre-clinical and clinical validation, this approach may support real-time decision-making and improve procedural outcomes.
Keywords: Acute Ischemic Sroke, Mechanical Thrombectomy (MT), stent retriever (SR), clot visualization, Machine Learning (ML), Computer Vision, Image segmentation - Deep learning, artificial intelligence - AI
Received: 11 Apr 2025; Accepted: 01 Oct 2025.
Copyright: Ā© 2025 Kashyap, Zhu and Narasimhan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Varun Kashyap, varun.kashyap@medtronic.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.