Robotic drilling was conducted with an accuracy of 0.2 mm and safety mechanisms predicted proximity of the nerves to within 0.1 mm. The approach resulted in a minimal mastoidectomy and minimal incisions. Manual electrode array insertion was successfully performed through the robotically drilled tunnel. The procedure was performed without complications, and all surrounding structures were preserved.

Surgical robot systems can work beyond the limits of human perception, dexterity and scale making them inherently suitable for use in microsurgical procedures. However, despite extensive research, image-guided robotics applications for microsurgery have seen limited introduction into clinical care to date. Among others, challenges are geometric scale and haptic resolution at which the surgeon cannot sufficiently control a device outside the range of human faculties. Mechanisms are required to ascertain redundant control on process variables that ensure safety of the device, much like instrument-flight in avionics. Cochlear implantation surgery is a microsurgical procedure, in which specific tasks are at sub-millimetric scale and exceed reliable visuo-tactile feedback. Cochlear implantation is subject to intra- and inter-operative variations, leading to potentially inconsistent clinical and audiological outcomes for patients. The concept of robotic cochlear implantation aims to increase consistency of surgical outcomes such as preservation of residual hearing and reduce invasiveness of the procedure. We report successful image-guided, robotic CI in human. The robotic treatment model encompasses: computer-assisted surgery planning, precision stereotactic image-guidance, in-situ assessment of tissue properties and multipolar neuromonitoring (NM), all based on in vitro, in vivo and pilot data. The model is expandable to integrate additional robotic functionalities such as cochlear access and electrode insertion. Our results demonstrate the feasibility and possibilities of using robotic technology for microsurgery on the lateral skull base. It has the potential for benefit in other microsurgical domains for which there is no task-oriented, robotic technology available at present.

The purpose-built robot system was able to perform a safe and reliable DCA for cochlear implantation. The workflow implemented in this study mimics the envisioned clinical procedure showing the feasibility of future clinical implementation.

To demonstrate the feasibility of robotic middle ear access in a clinical setting, nine adult patients with severe-to-profound hearing loss indicated for cochlear implantation were included in this clinical trial. A keyhole access tunnel to the tympanic cavity and targeting the round window was planned based on preoperatively acquired computed tomography image data and robotically drilled to the level of the facial recess. Intraoperative imaging was performed to confirm sufficient distance of the drilling trajectory to relevant anatomy. Robotic drilling continued toward the round window. The cochlear access was manually created by the surgeon. Electrode arrays were inserted through the keyhole tunnel under microscopic supervision via a tympanomeatal flap. All patients were successfully implanted with a cochlear implant. In 9 of 9 patients the robotic drilling was planned and performed to the level of the facial recess. In 3 patients, the procedure was reverted to a conventional approach for safety reasons. No change in facial nerve function compared to baseline measurements was observed. Robotic keyhole access for cochlear implantation is feasible. Further improvements to workflow complexity, duration of surgery, and usability including safety assessments are required to enable wider adoption of the procedure.

Image-guided microsurgery requires accuracies an order of magnitude higher than today's navigation systems provide. A critical step toward the achievement of such low-error requirements is a highly accurate and verified patient-to-image registration. With the aim of reducing target registration error to a level that would facilitate the use of image-guided robotic microsurgery on the rigid anatomy of the head, we have developed a semiautomatic fiducial detection technique. Automatic force-controlled localization of fiducials on the patient is achieved through the implementation of a robotic-controlled tactile search within the head of a standard surgical screw. Precise detection of the corresponding fiducials in the image data is realized using an automated model-based matching algorithm on high-resolution, isometric cone beam CT images. Verification of the registration technique on phantoms demonstrated that through the elimination of user variability, clinically relevant target registration errors of approximately 0.1 mm could be achieved.