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School of Engineering and Materials Science, Queen Mary University London, London E1 4NS, United Kingdom
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Eversion-based robotic navigation can reduce interaction forces by over 65%, enabling safer access to the spinal subarachnoid space without damaging delicate neural structures.
Standardized testbeds and effectiveness metrics could accelerate the development and validation of AI-assisted robotic thrombectomy, potentially revolutionizing stroke treatment accessibility.
