Robotic Applications in Cranial Neurosurgery: Current and Future

Evolution of Deep Brain Stimulation Techniques for Complication Mitigation

Complication mitigation in deep brain stimulation has been a topic matter of much discussion in the literature. In this article, we examine how neurosurgeons as individuals and as a field generated and adapted techniques to prevent infection, lead fracture/lead migration, and suboptimal outcomes in both the acute period and longitudinally. The authors performed a MEDLINE search inclusive of articles from 1987 to June 2023 including human studies written in English. Using the Rayyan platform, two reviewers (J.P. and R.M.) performed a title screen. Of the 776 articles, 252 were selected by title screen and 172 from abstract review for full-text evaluation. Ultimately, 124 publications were evaluated. We describe the initial complications and inefficiencies at the advent of deep brain stimulation and detail changes instituted by surgeons that reduced them. Furthermore, we discuss the trend in both undesired short-term and long-term outcomes with emphasis on how surgeons recognized and modified their practice to provide safer and better procedures. This scoping review adds to the literature as a guide to both new neurosurgeons and seasoned neurosurgeons alike to understand better what innovations have been trialed over time as we embark on novel targets and neuromodulatory technologies.

Text-to-video generative artificial intelligence: sora in neurosurgery

Artificial intelligence (AI) has increased in popularity in neurosurgery, with recent interest in generative AI algorithms such as the Large Language Model (LLM) ChatGPT. Sora, an innovation in generative AI, leverages natural language processing, deep learning, and computer vision to generate impressive videos from text prompts. This new tool has many potential applications in neurosurgery. These include patient education, public health, surgical training and planning, and research dissemination. However, there are considerable limitations to the current model such as physically implausible motion generation, spontaneous generation of subjects, unnatural object morphing, inaccurate physical interactions, and abnormal behavior presentation when many subjects are generated. Other typical concerns are with respect to patient privacy, bias, and ethics. Further, appropriate investigation is required to determine how effective generative videos are compared to their non-generated counterparts, irrespective of any limitations. Despite these challenges, Sora and other iterations of its text-to-video generative application may have many benefits to the neurosurgical community.

Robot-Assisted Minimally Invasive Asleep Single-Stage Deep Brain Stimulation Surgery: Operative Technique and Systematic Review

BACKGROUND AND OBJECTIVES

Robotic assistance has garnered increased use in neurosurgery. Recently, this has expanded to include deep brain stimulation (DBS). Several studies have reported increased accuracy and improved efficiency with robotic assistance, but these are limited to individual robotic platforms with smaller sample sizes or are broader studies on robotics not specific to DBS. Our objectives are to report our technique for robot-assisted, minimally invasive, asleep, single-stage DBS surgery and to perform a meta-analysis comparing techniques from previous studies.

METHODS

We performed a single-center retrospective review of DBS procedures using a floor-mounted robot with a frameless transient fiducial array registration. We compiled accuracy data (radial entry error, radial target error, and 3-dimensional target error) and efficiency data (operative time, setup time, and total procedure time). We then performed a meta-analysis of previous studies and compared these metrics.

RESULTS

We analyzed 315 electrodes implanted in 160 patients. The mean radial target error was 0.9 ± 0.5 mm, mean target 3-dimensional error was 1.3 ± 0.7 mm, and mean radial entry error was 1.1 ± 0.8 mm. The mean procedure time (including pulse generator placement) was 182.4 ± 47.8 minutes, and the mean setup time was 132.9 ± 32.0 minutes. The overall complication rate was 8.8% (2.5% hemorrhagic/ischemic, 2.5% infectious, and 0.6% revision). Our meta-analysis showed increased accuracy with floor-mounted over skull-mounted robotic platforms and with fiducial-based registrations over optical registrations.

CONCLUSION

Our technique for robot-assisted, minimally invasive, asleep, single-stage DBS surgery is safe, accurate, and efficient. Our data, combined with a meta-analysis of previous studies, demonstrate that robotic assistance can provide similar or increased accuracy and improved efficiency compared with traditional frame-based techniques. Our analysis also suggests that floor-mounted robots and fiducial-based registration methods may be more accurate.

Usefulness of Robotic Stereotactic Assistance (ROSA®) Device for Stereoelectroencephalography Electrode Implantation: A Systematic Review and Meta-analysis

The aim of this study was to systematically review and meta-analyze the efficiency and safety of using the Robotic Stereotactic Assistance (ROSA®) device (Zimmer Biomet; Warsaw, IN, USA) for stereoelectroencephalography (SEEG) electrode implantation in patients with drug-resistant epilepsy. Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a literature search was carried out. Overall, 855 nonduplicate relevant articles were determined, and 15 of them were selected for analysis. The benefits of the ROSA® device use in terms of electrode placement accuracy, as well as operative time length, perioperative complications, and seizure outcomes, were evaluated. Studies that were included reported on a total of 11,257 SEEG electrode implantations. The limited number of comparative studies hindered the comprehensive evaluation of the electrode implantation accuracy. Compared with frame-based or navigation-assisted techniques, ROSA®-assisted SEEG electrode implantation provided significant benefits for reduction of both overall operative time (mean difference [MD], -63.45 min; 95% confidence interval [CI] from -88.73 to -38.17 min; P < 0.00001) and operative time per implanted electrode (MD, -8.79 min; 95% CI from -14.37 to -3.21 min; P = 0.002). No significant differences existed in perioperative complications and seizure outcomes after the application of the ROSA® device and other techniques for electrode implantation. To conclude, the available evidence shows that the ROSA® device is an effective and safe surgical tool for trajectory-guided SEEG electrode implantation in patients with drug-resistant epilepsy, offering benefits for saving operative time and neither increasing the risk of perioperative complications nor negatively impacting seizure outcomes.

Design and Modeling of a Sub-2 mm Steerable Neuroendoscopic Grasping Tool

Minimally invasive procedures, such as endoscopic third ventriculostomy (ETV), benefit from the increased dexterity and safety that surgical continuum robots can bring. However, due to their natural compliance, new compatible end-effectors, such as graspers or scissors, must be developed and their actuation must be considered when developing the robotic structures in which they are housed due to the inherent coupling that will be introduced. In this paper, we integrate a tendon-driven meso-scale grasper, with a closed configuration diameter of 1.69 mm, into a 2 degree-of-freedom (DoF) tendon-driven neurosurgical robot with an outer diameter of less than 2 mm. Furthermore, the kinematics of the grasper is validated and an analysis of the coupling between the grasper and the robotic joints is conducted in order to evaluate the design performance.

Robotic-guided direct transtemporal embolization of an indirect carotid cavernous fistula

A middle-aged patient presented with right-sided chemosis, exophthalmos, and progressive visual loss. Digital subtraction angiography revealed a type D carotid-cavernous fistula (CCF). Transarterial embolization through the internal maxillary artery was unsuccessful, and there was no venous access to the CCF. A robotic-guided direct transtemporal embolization of the CCF with Onyx was performed, resulting in successful fistula obliteration and symptom resolution. This is the first reported case of a robotic-guided direct transcranial CCF embolization. We include a technical video that demonstrates this procedure (Supplemental File 1).

Surgical Robotics for Intracerebral Hemorrhage Treatment: State of the Art and Future Directions

Intracerebral hemorrhage (ICH) is a stroke subtype with high mortality and disability, and there are no proven medical treatments that can improve the functional outcome of ICH patients. Robot-assisted neurosurgery is a significant advancement in the development of minimally invasive surgery for ICH. This review encompasses the latest advances and future directions of surgical robots for ICH. First, three robotic systems for neurosurgery applied to ICH are illustrated. Second, the key technologies of robot-assisted surgery for ICH are introduced in aspects of stereotactic technique and navigation, the puncture instrument, and hematoma evacuation. Finally, the limitations of current surgical robots are summarized, and the possible development direction is discussed, which is named "multisensor fusion and intelligent aspiration control of minimally invasive surgical robot for ICH". It is expected that the new generation of surgical robots for ICH will facilitate quantitative, precise, individualized, standardized treatment strategies for ICH.

Artificial Intelligence for Neurosurgery : Current State and Future Directions

Artificial intelligence (AI) is a field of computer science that equips machines with human-like intelligence and enables them to learn, reason, and solve problems when presented with data in various formats. Neurosurgery is often at the forefront of innovative and disruptive technologies, which have similarly altered the course of acute and chronic diseases. In diagnostic imaging, such as X-rays, computed tomography, and magnetic resonance imaging, AI is used to analyze images. The use of robots in the field of neurosurgery is also increasing. In neurointensive care units, AI is used to analyze data and provide care to critically ill patients. Moreover, AI can be used to predict a patient's prognosis. Several AI applications have already been introduced in the field of neurosurgery, and many more are expected in the near future. Ultimately, it is our responsibility to keep pace with this evolution to provide meaningful outcomes and personalize each patient's care. Rather than blindly relying on AI in the future, neurosurgeons should gain a thorough understanding of it and use it to enhance their patient care.

Learning curves in robotic neurosurgery: a systematic review

The transition to performing procedures robotically generally entails a period of adjustment known as a learning curve as the surgeon develops a familiarity with the technology. However, no study has comprehensively examined robotic learning curves across the field of neurosurgery. We conducted a systematic review to characterize the scope of literature on robotic learning curves in neurosurgery, assess operative parameters that may involve a learning curve, and delineate areas for future investigation. PubMed, Embase, and Scopus were searched. Following deduplication, articles were screened by title and abstract for relevance. Remaining articles were screened via full text for final inclusion. Bibliographic and learning curve data were extracted. Of 746 resultant articles, 32 articles describing 3074 patients were included, of which 23 (71.9%) examined spine, 4 (12.5%) pediatric, 4 (12.5%) functional, and 1 (3.1%) general neurosurgery. The parameters assessed for learning curves were heterogeneous. In total, 8 (57.1%) of 14 studies found reduced operative time with increased cases, while the remainder demonstrated no learning curve. Six (60.0%) of 10 studies reported reduced operative time per component with increased cases, while the remainder indicated no learning curve. Radiation time, radiation time per component, robot time, registration time, setup time, and radiation dose were assessed by ≤ 4 studies each, with 0-66.7% of studies demonstrated a learning curve. Four (44.4%) of 9 studies on accuracy showed improvement over time, while the others indicated no improvement over time. The number of cases required to reverse the learning curve ranged from 3 to 75. Learning curves are common in robotic neurosurgery. However, existing studies demonstrate high heterogeneity in assessed parameters and the number of cases that comprise the learning curve. Future studies should seek to develop strategies to reduce the number of cases required to reach the learning curve.

Targeting the Future: Developing a Training Curriculum for Robotic Assisted Neurosurgery

OBJECTIVE

Technological advances have significantly fostered the use of robotics in neurosurgery. Due to their novelty, there is a need to develop training methods within neurosurgical residency programs that provide trainees the skills to utilize these systems in their future practices safely and effectively.

METHODS

We describe a detailed curriculum for trainees with significant responsibilities in the operating room, as well as hands-on and theoretical didactics. The curriculum for robot-assisted stereotactic electroencephalography (SEEG) and deep brain stimulation (DBS) electrode implantation technique and assessment tool has been designed based on Accreditation Council for Graduate Medical Education's (ACGME's) milestone requirement for surgical treatment of epilepsy and movement disorders. Residents were surveyed to assess their use of robotics in their surgical training.

RESULTS

Since 2019, more than 100 patients have undergone robot-assisted SEEG and DBS depth electrode implantations at our institution. Residents and fellows were involved in all aspects of surgical planning and execution and were encouraged to take an active role during procedures. Didactic sessions led by experienced faculty are emphasized as important learning tools prior to hands-on experience in the operating room. The results of the survey show that residents receive more training intraoperatively as compared to training sessions, yet trainees would benefit from more instruction on informative cadaveric simulation sessions.

CONCLUSIONS

Our curriculum was developed to become a structured tool for assessment of robotic education in neurosurgical training. This curriculum based on ACGME milestone requirements serve as a template for resident and fellow education in robotics in neurosurgery.

Deep Brain Stimulation: Emerging Tools for Simulation, Data Analysis, and Visualization

Deep brain stimulation (DBS) is a well-established neurosurgical procedure for movement disorders that is also being explored for treatment-resistant psychiatric conditions. This review highlights important consideration for DBS simulation and data analysis. The literature on DBS has expanded considerably in recent years, and this article aims to identify important trends in the field. During DBS planning, surgery, and follow up sessions, several large data sets are created for each patient, and it becomes clear that any group analysis of such data is a big data analysis problem and has to be handled with care. The aim of this review is to provide an update and overview from a neuroengineering perspective of the current DBS techniques, technical aids, and emerging tools with the focus on patient-specific electric field (EF) simulations, group analysis, and visualization in the DBS domain. Examples are given from the state-of-the-art literature including our own research. This work reviews different analysis methods for EF simulations, tractography, deep brain anatomical templates, and group analysis. Our analysis highlights that group analysis in DBS is a complex multi-level problem and selected parameters will highly influence the result. DBS analysis can only provide clinically relevant information if the EF simulations, tractography results, and derived brain atlases are based on as much patient-specific data as possible. A trend in DBS research is creation of more advanced and intuitive visualization of the complex analysis results suitable for the clinical environment.

Robotic and robot-assisted skull base neurosurgery: systematic review of current applications and future directions

OBJECTIVE

The utility of robotic instrumentation is expanding in neurosurgery. Despite this, successful examples of robotic implementation for endoscopic endonasal or skull base neurosurgery remain limited. Therefore, the authors performed a systematic review of the literature to identify all articles that used robotic systems to access the sella or anterior, middle, or posterior cranial fossae.

METHODS

A systematic review of MEDLINE and PubMed in accordance with PRISMA guidelines performed for articles published between January 1, 1990, and August 1, 2021, was conducted to identify all robotic systems (autonomous, semiautonomous, or surgeon-controlled) used for skull base neurosurgical procedures. Cadaveric and human clinical studies were included. Studies with exclusively otorhinolaryngological applications or using robotic microscopes were excluded.

RESULTS

A total of 561 studies were identified from the initial search, of which 22 were included following full-text review. Transoral robotic surgery (TORS) using the da Vinci Surgical System was the most widely reported system (4 studies) utilized for skull base and pituitary fossa procedures; additionally, it has been reported for resection of sellar masses in 4 patients. Seven cadaveric studies used the da Vinci Surgical System to access the skull base using alternative, non-TORS approaches (e.g., transnasal, transmaxillary, and supraorbital). Five cadaveric studies investigated alternative systems to access the skull base. Six studies investigated the use of robotic endoscope holders. Advantages to robotic applications in skull base neurosurgery included improved lighting and 3D visualization, replication of more traditional gesture-based movements, and the ability for dexterous movements ordinarily constrained by small operative corridors. Limitations included the size and angulation capacity of the robot, lack of drilling components preventing fully robotic procedures, and cost. Robotic endoscope holders may have been particularly advantageous when the use of a surgical assistant or second surgeon was limited.

CONCLUSIONS

Robotic skull base neurosurgery has been growing in popularity and feasibility, but significant limitations remain. While robotic systems seem to have allowed for greater maneuverability and 3D visualization, their size and lack of neurosurgery-specific tools have continued to prevent widespread adoption into current practice. The next generation of robotic technologies should prioritize overcoming these limitations.