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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.
Manual navigation outperforms robotic controllers in speed and safety, raising questions about the efficacy of haptic feedback in complex procedures.
Standardized testbeds and effectiveness metrics could accelerate the development and validation of AI-assisted robotic thrombectomy, potentially revolutionizing stroke treatment accessibility.
