Identifying research priorities for pituitary adenoma surgery: an international Delphi consensus statement.

Purpose: Pituitary surgery is the mainstay treatment for most pituitary adenomas, but many questions remain about perioperative and long-term management and outcomes. This study aimed to identify the most pressing research priorities in pituitary surgery with input from patients, caregivers, and healthcare professionals.

Methods: An initial survey of patients, caregivers, and healthcare professionals assembled priorities related to preoperative care, surgical techniques, and postoperative management in pituitary surgery.

Patient and Public Perceptions of Simulation Training in Neurosurgery: A Two-Stage Cross-Sectional Survey.

Background: The use of simulation in neurosurgery is a widespread and popular means of training worldwide. However, little is known about patient and public acceptability of simulation in neurosurgical training and the potential consequences of this for future simulation development.

Methods: A two-stage questionnaire strategy was utilized, the first gathering insights from neurosurgical inpatients, and the second from the general public.

Reappraising prediction of surgical complexity of non-functioning pituitary adenomas after transsphenoidal surgery: the modified TRANSSPHER grade.

Purpose: Prognostication of surgical complexity is crucial for optimizing decision-making and patient counseling in pituitary surgery. This study aimed to develop a clinical score to predict gross-total resection (GTR) in non-functioning pituitary adenomas (NFPAs) using externally validated machine-learning (ML) models.

Methods: Clinical and radiological data were collected from two tertiary medical centers.

Automated surgical skill assessment in endoscopic pituitary surgery using real-time instrument tracking on a high-fidelity bench-top phantom.

Improved surgical skill is generally associated with improved patient outcomes, although assessment is subjective, labour intensive, and requires domain-specific expertise. Automated data-driven metrics can alleviate these difficulties, as demonstrated by existing machine learning instrument tracking models. However, these models are tested on limited datasets of laparoscopic surgery, with a focus on isolated tasks and robotic surgery.

PitRSDNet: Predicting intra-operative remaining surgery duration in endoscopic pituitary surgery.

Accurate intra-operative Remaining Surgery Duration (RSD) predictions allow for anaesthetists to more accurately decide when to administer anaesthetic agents and drugs, as well as to notify hospital staff to send in the next patient. Therefore, RSD plays an important role in improved patient care and minimising surgical theatre costs via efficient scheduling. In endoscopic pituitary surgery, it is uniquely challenging due to variable workflow sequences with a selection of optional steps contributing to high variability in surgery duration.

Artificial intelligence assisted operative anatomy recognition in endoscopic pituitary surgery.

Pituitary tumours are surrounded by critical neurovascular structures and identification of these intra-operatively can be challenging. We have previously developed an AI model capable of sellar anatomy segmentation. This study aims to apply this model, and explore the impact of AI-assistance on clinician anatomy recognition.

Artificial Intelligence Assisted Surgical Scene Recognition: A Comparative Study Amongst Healthcare Professionals.

Objective: This study aimed to compare the ability of a deep-learning platform (the MACSSwin-T model) with healthcare professionals in detecting cerebral aneurysms from operative videos. Secondly, we aimed to compare the neurosurgical team's ability to detect cerebral aneurysms with and without AI-assistance.

Background: Modern microscopic surgery enables the capture of operative video data on an unforeseen scale.

Implanted cortical neuroprosthetics for speech and movement restoration.

Implanted cortical neuroprosthetics (ICNs) are medical devices developed to replace dysfunctional neural pathways by creating information exchange between the brain and a digital system which can facilitate interaction with the external world. Over the last decade, researchers have explored the application of ICNs for diverse conditions including blindness, aphasia, and paralysis. Both transcranial and endovascular approaches have been used to record neural activity in humans, and in a laboratory setting, high-performance decoding of the signals associated with speech intention has been demonstrated.

Skull Base Repair following Resection of Vestibular Schwannoma: A Systematic Review (Part 1: The Retrosigmoid Approach).

 Despite advances in skull-base reconstruction techniques, cerebrospinal fluid (CSF) leaks remain a common complication following retrosigmoid (RS) vestibular schwannoma (VS) surgery. We aimed to review and classify the available strategies used to prevent CSF leaks following RS VS surgery.  A systematic review, including studies of adults undergoing RS VS surgery since 2000, was conducted.

Skull Base Repair following Resection of Vestibular Schwannoma: A Systematic Review (Part 2: The Translabyrinthine Approach).

 Despite advances in skull base reconstruction techniques, cerebrospinal fluid (CSF) leaks remain a relatively common complication after translabyrinthine (TL) vestibular schwannoma (VS) surgery. We conducted a systematic review to synthesize the repair techniques and materials used in TL VS surgery to prevent CSF leaks.  A systematic review of studies published since 2000 reporting techniques to prevent CSF leaks during adult TL VS surgery was conducted.

Learning Curve of Endoscopic Lumbar Discectomy - A Systematic Review and Meta-Analysis of Individual Participant and Aggregated Data.

Study Design: A systematic review and meta-analysis of individual participant and aggregated data.

Objectives: To define the learning curves of endoscopic discectomies using unified statistical methodologies.

Methods: Searches returned 913 records, with 118 full-text articles screened.

Validation of a surgical simulator and establishment of quantitative performance thresholds-RealSpine simulation system for open lumbar decompressions.

Background Context: The majority of surgical training is conducted in real-world operations. High-fidelity surgical simulators may provide a safer environment for surgical training. However, the extent that it reflects real-world operations and surgical ability is often poorly characterized.

Video-Based Performance Analysis in Pituitary Surgery - Part 2: Artificial Intelligence Assisted Surgical Coaching.

Background: Superior surgical skill improves surgical outcomes in endoscopic pituitary adenoma surgery. Video-based coaching programs, pioneered in professional sports, have shown promise in surgical training. In this study, we developed and assessed a video-based coaching program using artificial intelligence (AI) assistance.

Video-Based Performance Analysis in Pituitary Surgery-Part 1: Surgical Outcomes.

Background: Endoscopic pituitary adenoma surgery has a steep learning curve, with varying surgical techniques and outcomes across centers. In other surgeries, superior performance is linked with superior surgical outcomes. This study aimed to explore the prediction of patient-specific outcomes using surgical video analysis in pituitary surgery.

Mapping the Clinical Pathway for Patients Undergoing Vestibular Schwannoma Resection.

Background: The introduction of the electronic health record (EHR) has improved the collection and storage of patient information, enhancing clinical communication and academic research. However, EHRs are limited by data quality and the time-consuming task of manual data extraction. This study aimed to use process mapping to help identify critical data entry points within the clinical pathway for patients with vestibular schwannoma (VS) ideal for structured data entry and automated data collection to improve patient care and research.

Empowering Early Career Neurosurgeons in the Critical Appraisal of Artificial Intelligence and Machine Learning: The Design and Evaluation of a Pilot Course.

Background: Artificial intelligence (AI) is expected to play a greater role in neurosurgery. There is a need for neurosurgeons capable of critically appraising AI literature to evaluate its implementation or communicate information to patients. However, there are a lack of courses delivered at a level appropriate for individuals to develop such skills.

Augmented reality for endoscopic transsphenoidal surgery: evaluating design factors with neurosurgeons.

Purpose: This study investigates the potential utility of augmented reality (AR) in the endoscopic transsphenoidal approach (TSA). While previous research has addressed technical challenges in AR for TSA, this paper explores how design factors can improve AR for neurosurgeons from a human-centred design perspective.

Methods: Preliminary qualitative research involved observations of TSA procedures ( ) and semi-structured interviews with neurosurgeons ( ).

Early evaluation of a natural language processing tool to improve access to educational resources for surgical patients.

Purpose: Accessible patient information sources are vital in educating patients about the benefits and risks of spinal surgery, which is crucial for obtaining informed consent. We aim to assess the effectiveness of a natural language processing (NLP) pipeline in recognizing surgical procedures from clinic letters and linking this with educational resources.

Methods: Retrospective examination of letters from patients seeking surgery for degenerative spinal disease at a single neurosurgical center.

Mapping patient education encounters in elective surgery: a cohort study and cross-sectional survey.

Objective: Develop a process map of when patients learn about their proposed surgery and what resources patients use to educate themselves.

Design: A mixed methods design, combining semistructured stakeholder interviews, quantitative validation using electronic healthcare records (EHR) in a retrospective cohort and a cross-sectional patient survey.

Setting: A single surgical centre in the UK.

Artificial intelligence in histopathological image analysis of central nervous system tumours: A systematic review.

The convergence of digital pathology and artificial intelligence could assist histopathology image analysis by providing tools for rapid, automated morphological analysis. This systematic review explores the use of artificial intelligence for histopathological image analysis of digitised central nervous system (CNS) tumour slides. Comprehensive searches were conducted across EMBASE, Medline and the Cochrane Library up to June 2023 using relevant keywords.