Advances in robotic medicine have been adopted by various surgical subspecialties as the benefits of this technology become more readily apparent: precision in narrow operative windows, tremor controlled movements, and modestly improved outcomes, among others.
Robotic manipulators easily allow to achieve great precision, reliability, and rapidity in the positioning of surgical instruments or devices in the brain. The aim of a work was to experimentally verify a fully automatic “no hands” surgical procedure. The integration of neuroimaging to data for planning the surgery, followed by application of new specific surgical tools, permitted the realization of a fully automated robotic implantation of leads in brain targets. An anthropomorphic commercial manipulator was utilized. In a preliminary phase, a software to plan surgery was developed, and the surgical tools were tested first during a simulation and then on a skull mock-up. In such a way, several tools were developed and tested, and the basis for an innovative surgical procedure arose. The final experimentation was carried out on anesthetized “large white” pigs. The determination of stereotactic parameters for the correct planning to reach the intended target was performed with the same technique currently employed in human stereotactic neurosurgery, and the robotic system revealed to be reliable and precise in reaching the target. The results of this work strengthen the possibility that a neurosurgeon may be substituted by a machine, and may represent the beginning of a new approach in the current clinical practice. Moreover, this possibility may have a great impact not only on stereotactic functional procedures but also on the entire domain of neurosurgery 1).
In the case of surgery of the skull base, it has just emerged from an experimental phase.
Neurosurgery is one of the first organ systems in which robotic surgery can play a role, due to the high precision that is required to localize and manipulate within the brain, and the relatively fixed landmarks of the cranial anatomy.
Interest in robotic endoscopic surgery is high because of the small size of the incisions, cosmetic advantages, less invasive surgical techniques, decreased scar tissue, shorter duration of hospitalization and increased cost-effectiveness 4).
see Stereotactic robot.
see Robotic forceps.
Vascular neurosurgery, in particular, remains open to newer and more cutting edge treatment options for complex pathologies, and robotics may be on the horizon for such advances.
Menaker et al. from the Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, Florida, USA. seeked to provide a broad overview of these innovations in vascular neurosurgery for both practitioners well acquainted with robotics and those seeking to become more familiar. Technologies under development for cerebrovascular and endovascular neurosurgery include robot assisted angiography, guided operative microscopes, coil insertion systems, and endoscopic clipping devices. Additionally, robotic systems in the fields of interventional cardiology and radiology have potential applications to endovascular neurosurgery but require proper modifications to navigate complex intracerebral vasculature. Robotic technology is not without drawbacks, as broad implementation may lead to increased cost, training time, and potential delays in emergency situations. Further cultivation of current multidisciplinary technologies and investment into newer systems is necessary before robotics can make a sizable impact in clinical practice 8).
Current markerless registration methods for neurosurgical robotics use the facial surface to match the robot space with the image space, and acquisition of the facial surface usually requires manual interaction and constrains the patient to a supine position. To overcome these drawbacks, we propose a registration method that is automatic and does not constrain patient position. METHODS: An optical camera attached to the robot end effector captures images around the patient's head from multiple views. Then, high coverage of the head surface is reconstructed from the images through multi-view stereo vision. Since the acquired head surface point cloud contains color information, a specific mark that is manually drawn on the patient's head prior to the capture procedure can be extracted to automatically accomplish coarse registration rather than using facial anatomic landmarks. Then, fine registration is achieved by registering the high coverage of the head surface without relying solely on the facial region, thus eliminating patient position constraints. RESULTS: The head surface was acquired by the camera with a good repeatability accuracy. The average target registration error of 8 different patient positions measured with targets inside a head phantom was [Formula: see text], while the mean surface registration error was [Formula: see text]. CONCLUSION: The method proposed in this paper achieves automatic markerless registration in multiple patient positions and guarantees registration accuracy inside the head. This method provides a new approach for establishing the spatial relationship between the image space and the robot space 9).