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Published on: January 6, 2023
The Evolution and Application of Robotic Systems in Neurotologic Surgery: A Scoping Review
Jonathan Wang1, Andy S Ding, Oren Wei
1Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins Hospital, Baltimore, MD.
Objective:
To map the technological evolution and clinical feasibility of robotic systems in neurotologic and lateral skull-base surgery, identifying bottlenecks limiting widespread clinical translation.
Databases Reviewed:
A systematic search was conducted across Embase, Scopus, PubMed/MEDLINE, Web of Science, and the Cochrane Central Register of Controlled Trials.
Review Methods:
A Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews-compliant scoping review was conducted. Inclusion criteria comprised peer-reviewed, English-language original research evaluating the feasibility, accuracy, and clinical performance of robotic systems in human subjects or cadaveric models undergoing neurotologic procedures. Data regarding study characteristics, spatial accuracy, system kinematics, and safety outcomes were extracted for qualitative synthesis.
Results:
Fifty-seven studies met the inclusion criteria. Notably, the yielded literature was skewed toward otologic applications rather than broader lateral skull-base resections. Two dominant clinical applications emerged: image-guided bone milling (eg, direct cochlear access) and robotic-assisted microinstrumentation (eg, cochlear implant electrode insertion). Robotic milling systems consistently achieved submillimetric accuracy (frequently <0.15 mm at the facial recess) by integrating multilayer safety architectures, including force-torque sensing and continuous electromyography. For electrode insertion, motorized actuation standardized ultra-slow velocities (0.1 to 0.25 mm/s), significantly reducing scalar translocations and maximum insertion forces compared with manual techniques. Primary barriers to broad adoption include prolonged operative setup times, reliance on invasive fiducial registration, and an absence of high-fidelity active haptic feedback.
Conclusion:
Robotic systems in neurotology successfully mitigate physiological human tremor and enforce strict kinematic safety boundaries, enabling advanced, minimally invasive approaches. Widespread clinical integration will depend on transitioning to markerless registration, validating in vivo dynamic models, and overcoming economic and workflow barriers.
