Fluorophores in Endoscopic Neurosurgery.

The complexity of intracranial anatomy and pathologies warrants the optimization of multimodal techniques to ensure safe and effective surgical treatment. Endoscopy is being more widely implemented in intracranial procedures as an important visualization tool, as it can offer panoramic views of deep structures while reducing the invasiveness of approaches. Fluorophores are frequently utilized to augment the identification of intracranial anatomic landmarks and pathologies. This chapter discusses the integration of these two surgical adjuncts, highlighting the key fluorophores used in endoscopic neurosurgery and their clinical applications.

Endoscopic Microvascular Decompression.

Microvascular decompression is a widely accepted surgical treatment for compressive cranial nerve pathologies such as trigeminal neuralgia, hemifacial spasm, glossopharyngeal neuralgia, and other craniofacial pain syndromes. Endoscopy has risen as a safe and effective minimally invasive tool to optimize microvascular decompression. Endoscopy offers improved visualization, minimizes retraction, and allows for smaller surgical openings compared to traditional microscopic approaches. There are several reports of improved neuralgia outcomes and reduced post-operative complications after endoscopic microvascular decompression. In skilled surgical hands, endoscopy is an excellent option for microvascular decompression as stand-alone tool or adjunct to the microscope. An overview of the history, operative considerations, and techniques is provided in this chapter.

A multi-institutional machine learning algorithm for prognosticating facial nerve injury following microsurgical resection of vestibular schwannoma.

Vestibular schwannomas (VS) are the most common tumor of the skull base with available treatment options that carry a risk of iatrogenic injury to the facial nerve, which can significantly impact patients' quality of life. As facial nerve outcomes remain challenging to prognosticate, we endeavored to utilize machine learning to decipher predictive factors relevant to facial nerve outcomes following microsurgical resection of VS. A database of patient-, tumor- and surgery-specific features was constructed via retrospective chart review of 242 consecutive patients who underwent microsurgical resection of VS over a 7-year study period. This database was then used to train non-linear supervised machine learning classifiers to predict facial nerve preservation, defined as House-Brackmann (HB) I vs. facial nerve injury, defined as HB II-VI, as determined at 6-month outpatient follow-up. A random forest algorithm demonstrated 90.5% accuracy, 90% sensitivity and 90% specificity in facial nerve injury prognostication. A random variable (rv) was generated by randomly sampling a Gaussian distribution and used as a benchmark to compare the predictiveness of other features. This analysis revealed age, body mass index (BMI), case length and the tumor dimension representing tumor growth towards the brainstem as prognosticators of facial nerve injury. When validated via prospective assessment of facial nerve injury risk, this model demonstrated 84% accuracy. Here, we describe the development of a machine learning algorithm to predict the likelihood of facial nerve injury following microsurgical resection of VS. In addition to serving as a clinically applicable tool, this highlights the potential of machine learning to reveal non-linear relationships between variables which may have clinical value in prognostication of outcomes for high-risk surgical procedures.

Pain Outcomes Following Endoscopic Microvascular Decompression for Trigeminal Neuralgia Based on Vascular Compression Type.

Background Arterial compression of the trigeminal nerve at the root entry zone has been the long-attributed cause of compressive trigeminal neuralgia despite numerous studies reporting distal and/or venous compression. The impact of compression type on patient outcomes has not been fully elucidated. Objective We categorized vascular compression (VC) based on vessel and location of compression to correlate pain outcomes based on compression type. Methods A retrospective video review of 217 patients undergoing endoscopic microvascular decompression for trigeminal neuralgia categorizing VC into five distinct types, proximal arterial compression (VC1), proximal venous compression (VC2), distal arterial compression (VC3), distal venous compression (VC4), and no VC (VC5). VC type was correlated with postoperative pain outcomes at 1 month ( n = 179) and last follow-up (mean = 42.9 mo, n = 134). Results At 1 month and longest follow-up, respectively, pain was rated as "much improved" or "very much improved" in 89 69% of patients with VC1, 86.6 and 62.5% of patients with VC2, 100 and 87.5% of patients with VC3, 83 and 62.5% of patients with VC4, and 100 and 100% of patients with VC5. Multivariate analysis demonstrated VC4 as a significant negative of predictor pain outcomes at 1 month, but not longest follow-up, and advanced age as a significant positive predictor. Conclusion The degree of clinical improvement in all types of VC was excellent, but at longest follow-up VC type was not a significant predictor out outcome. However distal venous compression was significantly associated with worse outcomes at 1 month.

Should I See You Again Soon? Multispecialty Assessment of Impact and Burden of Preoperative History and Physical Update Visits.

BACKGROUND: Federal regulations require a history and physical (H&P) update performed 30 days or less before a planned procedure. We evaluated the use and burdens of H&P update visits by determining impact on operative management, suitability for telehealth, and visit time and travel burden. STUDY DESIGN: We identified H&P update visits performed in our health system during 2019 for 8 surgical specialties. As available, up to 50 visits per specialty were randomly selected. Primary outcomes were interval changes in history, examination, or operative plan between the initial and updated H&P notes, and visit suitability for telehealth, as determined by 2 independent physician reviewers. Clinic time was captured, and round-trip driving time and distance between patients' home and clinic ZIP codes were estimated. RESULTS: We identified 8,683 visits and 362 were randomly selected for review. Documented changes were most commonly identified in histories (60.8%), but rarely in physical examinations (11.9%) and operative plans (11.6%). Of 362 visits, 359 (99.2%) visits were considered suitable for telehealth. Median clinic time was 52 minutes (interquartile range 33.8 to 78), driving time was 55.6 minutes (interquartile range 35.5 to 85.5), and driving distance was 20.2 miles (interquartile range 8.5 to 38.4). At the health system level, patients spent an estimated aggregate 7,000 hours (including 4,046 hours of waiting room and travel time) and drove 142,273 miles to attend in-person H&P update visits in 2019. CONCLUSIONS: Given their minimal impact on operative management, regulatory requirements for in-person H&P updates should be reconsidered. Flexibility in update timing and modality might help defray the substantial burdens these visits impose on patients.

Multi-institutional Analysis of Endoscopic Sellar Surgical Volumes During the COVID-19 Pandemic.

OBJECTIVE: We sought to quantify trends in operative volumes and complications of endoscopic sellar surgery before and after the COVID-19 pandemic onset. STUDY DESIGN: We performed a retrospective analysis. SETTING: TriNetX database analysis. METHODS: All adults undergoing neuroendoscopy for resection of pituitary tumor (Current Procedural Terminology code 62165) with diagnosis of benign/malignant neoplasm of pituitary gland (D35.2/C75.1) or benign/malignant neoplasm of craniopharyngeal duct (D35.3/C75.2) were included using the TriNetX database for 2 years before (pre-COVID group) and 2 years after (post-COVID group) February 17, 2020. RESULTS: A total of 1238 patients in the pre-COVID group and 1186 patients in the post-COVID group were compared. Age, gender, and race were statistically similar between the groups (P > .05). Surgical volume decreased by 6% in the post-COVID group. In 2020 Q2, operative volume decreased by 19%, and in 2021 Q4 (peak COVID-19 caseload in the United States), operative volumes decreased by 29% compared to 2 years prior. Postoperative complications including meningitis (P = .49), cerebrospinal fluid leak (P = .36), visual field deficits (P = .07), postoperative pneumonia or respiratory failure (P = .42), and 30-day readmission rates (P = .89) were similar between the 2 groups. CONCLUSION: Overall, endoscopic sellar surgery may continue to fluctuate with increased COVID-19 outbreaks. Patient outcomes do not appear to be worsened by decreased operative volumes or delays in nonurgent surgeries.

Developing the surgeon-machine interface: using a novel instance-segmentation framework for intraoperative landmark labelling.

Introduction: The utilisation of artificial intelligence (AI) augments intraoperative safety, surgical training, and patient outcomes. We introduce the term Surgeon-Machine Interface (SMI) to describe this innovative intersection between surgeons and machine inference. A custom deep computer vision (CV) architecture within a sparse labelling paradigm was developed, specifically tailored to conceptualise the SMI. This platform demonstrates the ability to perform instance segmentation on anatomical landmarks and tools from a single open spinal dural arteriovenous fistula (dAVF) surgery video dataset. Methods: Our custom deep convolutional neural network was based on SOLOv2 architecture for precise, instance-level segmentation of surgical video data. Test video consisted of 8520 frames, with sparse labelling of only 133 frames annotated for training. Accuracy and inference time, assessed using F1-score and mean Average Precision (mAP), were compared against current state-of-the-art architectures on a separate test set of 85 additionally annotated frames. Results: Our SMI demonstrated superior accuracy and computing speed compared to these frameworks. The F1-score and mAP achieved by our platform were 17% and 15.2% respectively, surpassing MaskRCNN (15.2%, 13.9%), YOLOv3 (5.4%, 11.9%), and SOLOv2 (3.1%, 10.4%). Considering detections that exceeded the Intersection over Union threshold of 50%, our platform achieved an impressive F1-score of 44.2% and mAP of 46.3%, outperforming MaskRCNN (41.3%, 43.5%), YOLOv3 (15%, 34.1%), and SOLOv2 (9%, 32.3%). Our platform demonstrated the fastest inference time (88ms), compared to MaskRCNN (90ms), SOLOV2 (100ms), and YOLOv3 (106ms). Finally, the minimal amount of training set demonstrated a good generalisation performance -our architecture successfully identified objects in a frame that were not included in the training or validation frames, indicating its ability to handle out-of-domain scenarios. Discussion: We present our development of an innovative intraoperative SMI to demonstrate the future promise of advanced CV in the surgical domain. Through successful implementation in a microscopic dAVF surgery, our framework demonstrates superior performance over current state-of-the-art segmentation architectures in intraoperative landmark guidance with high sample efficiency, representing the most advanced AI-enabled surgical inference platform to date. Our future goals include transfer learning paradigms for scaling to additional surgery types, addressing clinical and technical limitations for performing real-time decoding, and ultimate enablement of a real-time neurosurgical guidance platform.

The Device for Intraventricular Entry guide: a novel solution to a perpetual problem.

OBJECTIVE: The authors designed a low-profile device for reliable ventricular access and prospectively studied its safety, efficacy, and accuracy at a large academic center. METHODS: A novel device for ventricular entry, the Device for Intraventricular Entry (DIVE) guide, was designed and created by the first and senior authors. Fifty patients undergoing external ventricular drainage (EVD) or shunt placement were prospectively enrolled for DIVE-assisted catheter placement at a single academic center. The primary outcome was the catheter tip location on postprocedural CT. Secondary outcomes included number of catheter passes, clinically significant hemorrhages, and procedure-related infections. RESULTS: Fifty patients were enrolled. Indications included subarachnoid hemorrhage, intraventricular hemorrhage, traumatic brain injury, hydrocephalus, pseudotumor, and postsurgical wound drainage. In total, 76% (38/50) of patients underwent right-sided placement and 24% (12/50) underwent left-sided placement. All 100% (50/50) of patients had successful cannulation with an average of 1.06 passes. Postprocedural head CT confirmed ipsilateral frontal horn or third ventricle placement (Kakarla grade 1) in 92% (46/50) of patients and placement in the contralateral lateral ventricle in 8% (4/50) (Kakarla grade 2). There were no clinically significant track hemorrhages or procedural infections. CONCLUSIONS: This single-center prospective study investigated the safety and efficacy of DIVE-assisted ventricular access. In total, 100% of procedures had successful ventricular cannulation, with 92% achieving Kakarla grade 1, with an average of 1.06 passes without any clinical complications.

Large Language Model-Based Neurosurgical Evaluation Matrix: A Novel Scoring Criteria to Assess the Efficacy of ChatGPT as an Educational Tool for Neurosurgery Board Preparation.

INTRODUCTION: Technological advancements are reshaping medical education, with digital tools becoming essential in all levels of training. Amidst this transformation, the study explores the potential of ChatGPT, an artificial intelligence model by OpenAI, in enhancing neurosurgical board education. The focus extends beyond technology adoption to its effective utilization, with ChatGPT's proficiency evaluated against practice questions from the Primary Neurosurgery Written Board Exam. METHODS: Using the Congress of Neurologic Surgeons (CNS) Self-Assessment Neurosurgery (SANS) Exam Board Review Prep questions, we conducted 3 rounds of analysis with ChatGPT. We developed a novel ChatGPT Neurosurgical Evaluation Matrix (CNEM) to assess the output quality, accuracy, concordance, and clarity of ChatGPT's answers. RESULTS: ChatGPT achieved spot-on accuracy for 66.7% of prompted questions, 59.4% of unprompted questions, and 63.9% of unprompted questions with a leading phrase. Stratified by topic, accuracy ranged from 50.0% (Vascular) to 78.8% (Neuropathology). In comparison to SANS explanations, ChatGPT output was considered better in 19.1% of questions, equal in 51.6%, and worse in 29.3%. Concordance analysis showed that 95.5% of unprompted ChatGPT outputs and 97.4% of unprompted outputs with a leading phrase were aligned. CONCLUSIONS: Our study evaluated the performance of ChatGPT in neurosurgical board education by assessing its accuracy, clarity, and concordance. The findings highlight the potential and challenges of integrating AI technologies like ChatGPT into medical and neurosurgical board education. Further research is needed to refine these tools and optimize their performance for enhanced medical education and patient care.

Protocol for human brain organoid transplantation into a rat visual cortex to model neural repair.

Human stem-cell-derived organoids represent a promising substrate for transplantation-based neural repair. Here, we describe a protocol for transplanting forebrain organoids into an injured adult rat visual cortex. This protocol includes surgical details for craniectomy, aspiration injury, organoid transplantation, and cranioplasty. This platform represents a valuable tool for investigating the efficacy of organoids as structured grafts for neural repair. For complete details on the use and execution of this protocol, please refer to Jgamadze et al.1.