Improving risk communication: a proof-of-concept randomised control trial assessing the impact of visual aids for neurosurgical consent

IntroductionInformed consent is a fundamental component in the work-up for surgical procedures. Statistical risk information pertaining to a procedure is by nature probabilistic and challenging to communicate, especially to those with poor numerical literacy. Visual aids and audio/video tools have previously been shown to improve patients' understanding of statistical information. In this study, we aimed to explore the impact of different methods of risk communication in healthy participants randomized to either undergo the consent process with visual aids or the standard consent process for lumbar puncture.Material and methodsHealthy individuals above 18 years old were eligible. The exclusion criteria were prior experience of the procedure or relevant medical knowledge, lack of capacity to consent, underlying cognitive impairment and hospitalised individuals. After randomisation, both groups received identical medical information about the procedure of a lumbar puncture in a hypothetical clinical scenario via different means of consent. The control group underwent the standard consent process in current clinical practice (Consent Form 1 without any illustrative examples), whereas the intervention group received additional anatomy diagrams, the Paling Palette and the Paling perspective scale. Anonymised questionnaires were received to evaluate their perception of the procedure and its associated risks.ResultsFifty-two individuals were eligible without statistically significant differences in age, sex, professional status and the familiarity of the procedure. Visual aids were noted to improve the confidence of participants to describe the risks by themselves (p = 0.009) and participants in the intervention group felt significantly less overwhelmed with medical information (p = 0.028). The enhanced consent process was found to be significantly more acceptable by participants (p = 0.03). There was a trend towards greater appropriateness (p = 0.06) and it appeared to have “good” usability (median SUS = 76.4), although this also did not reach statistical significance (p = 0.06)ConclusionVisual aids could be an appropriate alternative method for medical consent without being inferior regarding the understanding of the procedure, its risks and its benefits. Future studies could possibly compare or incorporate multiple interventions to determine the most effective tools in a larger scale of population including patients as well as healthy individuals

Post-Operative Medium- and Long-Term Endocrine Outcomes in Patients with Non-Functioning Pituitary Adenomas—Machine Learning Analysis

Post-operative endocrine outcomes in patients with non-functioning pituitary adenoma (NFPA) are variable. The aim of this study was to use machine learning (ML) models to better predict medium- and long-term post-operative hypopituitarism in patients with NFPAs. We included data from 383 patients who underwent surgery with or without radiotherapy for NFPAs, with a follow-up period between 6 months and 15 years. ML models, including k-nearest neighbour (KNN), support vector machine (SVM), and decision tree models, showed a superior ability to predict panhypopituitarism compared with non-parametric statistical modelling (mean accuracy: 0.89; mean AUC-ROC: 0.79), with SVM achieving the highest performance (mean accuracy: 0.94; mean AUC-ROC: 0.88). Pre-operative endocrine function was the strongest feature for predicting panhypopituitarism within 1 year post-operatively, while endocrine outcomes at 1 year post-operatively supported strong predictions of panhypopituitarism at 5 and 10 years post-operatively. Other features found to contribute to panhypopituitarism prediction were age, volume of tumour, and the use of radiotherapy. In conclusion, our study demonstrates that ML models show potential in predicting post-operative panhypopituitarism in the medium and long term in patients with NFPM. Future work will include incorporating additional, more granular data, including imaging and operative video data, across multiple centres

Reducing Prediction volatility in the surgical workflow recognition of endoscopic pituitary surgery

PURPOSE: Workflow recognition can aid surgeons before an operation when used as a training tool, during an operation by increasing operating room efficiency, and after an operation in the completion of operation notes. Although several methods have been applied to this task, they have been tested on few surgical datasets. Therefore, their generalisability is not well tested, particularly for surgical approaches utilising smaller working spaces which are susceptible to occlusion and necessitate frequent withdrawal of the endoscope. This leads to rapidly changing predictions, which reduces the clinical confidence of the methods, and hence limits their suitability for clinical translation. METHODS: Firstly, the optimal neural network is found using established methods, using endoscopic pituitary surgery as an exemplar. Then, prediction volatility is formally defined as a new evaluation metric as a proxy for uncertainty, and two temporal smoothing functions are created. The first (modal, Mn) mode-averages over the previous n predictions, and the second (threshold, Tn) ensures a class is only changed after being continuously predicted for n predictions. Both functions are independently applied to the predictions of the optimal network. RESULTS: The methods are evaluated on a 50-video dataset using fivefold cross-validation, and the optimised evaluation metric is weighted-F1 score. The optimal model is ResNet-50+LSTM achieving 0.84 in 3-phase classification and 0.74 in 7-step classification. Applying threshold smoothing further improves these results, achieving 0.86 in 3-phase classification, and 0.75 in 7-step classification, while also drastically reducing the prediction volatility. CONCLUSION: The results confirm the established methods generalise to endoscopic pituitary surgery, and show simple temporal smoothing not only reduces prediction volatility, but actively improves performance

Efficacy of a Mindfulness-Based Intervention in Ameliorating Inattentional Blindness Amongst Young Neurosurgeons: A Prospective, Controlled Pilot Study

BACKGROUND: Human factors are increasingly being recognised as vital components of safe surgical care. One such human cognitive factor: inattention blindness (IB), describes the inability to perceive objects despite being visible, typically when one’s attention is focused on another task. This may contribute toward operative ‘never-events’ such as retained foreign objects and wrong-site surgery. METHODS: An 8-week, mindfulness-based intervention (MBI) programme, adapted for surgeons, was delivered virtually. Neurosurgical trainees and recent staff-appointees who completed the MBI were compared against a control group, matched in age, sex and grade. Attention and IB were tested using two operative videos. In each, participants were first instructed to focus on a specific part of the procedure and assessed (attention), then questioned on a separate but easily visible aspect within the operative field (inattention). If a participant were ‘inattentionally blind’ they would miss significant events occurring outside of their main focus. Median absolute error (MAE) scores were calculated for both attention and inattention. A generalised linear model was fitted for each, to determine the independent effect of mindfulness intervention on MAE. RESULTS: Thirteen neurosurgeons completed the mindfulness training (age, 30 years [range 27–35]; female:male, 5:8), compared to 15 neurosurgeons in the control group (age, 30 years [27–42]; female:male, 6:9). There were no significant demographic differences between groups. MBI participants demonstrated no significant differences on attention tasks as compared to controls (t = −1.50, p = 0.14). For inattention tasks, neurosurgeons who completed the MBI had significantly less errors (t = −2.47, p = 0.02), after adjusting for participant level and video differences versus controls. We found that both groups significantly improved their inattention error rate between videos (t = −11.37, p < 0.0001). In spite of this, MBI participants still significantly outperformed controls in inattention MAE in the second video following post-hoc analysis (MWU = 137.5, p = 0.05). DISCUSSION: Neurosurgeons who underwent an eight-week MBI had significantly reduced inattention blindness errors as compared to controls, suggesting mindfulness as a potential tool to increase vigilance and prevent operative mistakes. Our findings cautiously support further mindfulness evaluation and the implementation of these techniques within the neurosurgical training curriculum

Shifted-windows transformers for the detection of cerebral aneurysms in microsurgery

Purpose: Microsurgical Aneurysm Clipping Surgery (MACS) carries a high risk for intraoperative aneurysm rupture. Automated recognition of instances when the aneurysm is exposed in the surgical video would be a valuable reference point for neuronavigation, indicating phase transitioning and more importantly designating moments of high risk for rupture. This article introduces the MACS dataset containing 16 surgical videos with frame-level expert annotations and proposes a learning methodology for surgical scene understanding identifying video frames with the aneurysm present in the operating microscope’s field-of-view./ Methods: Despite the dataset imbalance (80% no presence, 20% presence) and developed without explicit annotations, we demonstrate the applicability of Transformer-based deep learning architectures (MACSSwin-T, vidMACSSwin-T) to detect the aneurysm and classify MACS frames accordingly. We evaluate the proposed models in multiple-fold cross-validation experiments with independent sets and in an unseen set of 15 images against 10 human experts (neurosurgeons)./ Results: Average (across folds) accuracy of 80.8% (range 78.5–82.4%) and 87.1% (range 85.1–91.3%) is obtained for the image- and video-level approach, respectively, demonstrating that the models effectively learn the classification task. Qualitative evaluation of the models’ class activation maps shows these to be localized on the aneurysm’s actual location. Depending on the decision threshold, MACSWin-T achieves 66.7–86.7% accuracy in the unseen images, compared to 82% of human raters, with moderate to strong correlation./ Conclusions: Proposed architectures show robust performance and with an adjusted threshold promoting detection of the underrepresented (aneurysm presence) class, comparable to human expert accuracy. Our work represents the first step towards landmark detection in MACS with the aim to inform surgical teams to attend to high-risk moments, taking precautionary measures to avoid rupturing

Image-guidance in endoscopic pituitary surgery: an in-silico study of errors involved in tracker-based techniques

Background: Endoscopic endonasal surgery is an established minimally invasive technique for resecting pituitary adenomas. However, understanding orientation and identifying critical neurovascular structures in this anatomically dense region can be challenging. In clinical practice, commercial navigation systems use a tracked pointer for guidance. Augmented Reality (AR) is an emerging technology used for surgical guidance. It can be tracker based or vision based, but neither is widely used in pituitary surgery. Methods: This pre-clinical study aims to assess the accuracy of tracker-based navigation systems, including those that allow for AR. Two setups were used to conduct simulations: (1) the standard pointer setup, tracked by an infrared camera; and (2) the endoscope setup that allows for AR, using reflective markers on the end of the endoscope, tracked by infrared cameras. The error sources were estimated by calculating the Euclidean distance between a point’s true location and the point’s location after passing it through the noisy system. A phantom study was then conducted to verify the in-silico simulation results and show a working example of image-based navigation errors in current methodologies. Results: The errors of the tracked pointer and tracked endoscope simulations were 1.7 and 2.5 mm respectively. The phantom study showed errors of 2.14 and 3.21 mm for the tracked pointer and tracked endoscope setups respectively. Discussion: In pituitary surgery, precise neighboring structure identification is crucial for success. However, our simulations reveal that the errors of tracked approaches were too large to meet the fine error margins required for pituitary surgery. In order to achieve the required accuracy, we would need much more accurate tracking, better calibration and improved registration techniques

Current and Future Advances in Surgical Therapy for Pituitary Adenoma

The vital physiological role of the pituitary gland, alongside its proximal critical neurovascular structures means pituitary adenomas cause significant morbidity or mortality. Whilst enormous advancements have been made in the surgical care of pituitary adenomas, treatment failure and recurrence remain challenges. To meet these clinical challenges, there has been an enormous expansion of novel medical technologies (e.g. endoscopy, advanced imaging, artificial intelligence). These innovations have the potential to benefit each step of the patient journey, and ultimately, drive improved outcomes. Earlier and more accurate diagnosis addresses this in part. Analysis of novel patient data sets, such as automated facial analysis or natural language processing of medical records holds potential in achieving an earlier diagnosis. After diagnosis, treatment decision-making and planning will benefit from radiomics and multimodal machine learning models. Surgical safety and effectiveness will be transformed by smart simulation methods for trainees. Next-generation imaging techniques and augmented reality will enhance surgical planning and intraoperative navigation. Similarly, the future armamentarium of pituitary surgeons, including advanced optical devices, smart instruments and surgical robotics, will augment the surgeon's abilities. Intraoperative support to team members will benefit from a surgical data science approach, utilising machine learning analysis of operative videos to improve patient safety and orientate team members to a common workflow. Postoperatively, early detection of individuals at risk of complications and prediction of treatment failure through neural networks of multimodal datasets will support earlier intervention, safer hospital discharge, guide follow-up and adjuvant treatment decisions. Whilst advancements in pituitary surgery hold promise to enhance the quality of care, clinicians must be the gatekeepers of technological translation, ensuring systematic assessment of risk and benefit. In doing so, the synergy between these innovations can be leveraged to drive improved outcomes for patients of the future

Specialised Surgical Instruments for Endoscopic and Endoscope-Assisted Neurosurgery: A Systematic Review of Safety, Efficacy and Usability

While there have been great strides in endoscopic and endoscope-assisted neurosurgical approaches, particularly in the treatment of deep-sited brain and skull base tumours, the greatest technical barrier to their adoption has been the availability of suitable surgical instruments. This systematic review seeks to identify specialised instruments for these approaches and evaluate their safety, efficacy and usability. Conducted in accordance with the PRISMA guidelines, Medline, Embase, CENTRAL, SCOPUS and Web of Science were searched. Original research studies that reported the use of specialised mechanical instruments that manipulate tissue in human patients, cadavers or surgical models were included. The results identified 50 specialised instruments over 62 studies. Objective measures of safety were reported in 32 out of 62 studies, and 20 reported objective measures of efficacy. Instruments were broadly safe and effective with one instrument malfunction noted. Measures of usability were reported in 15 studies, with seven reporting on ergonomics and eight on the instruments learning curve. Instruments with reports on usability were generally considered to be ergonomic, though learning curve was often considered a disadvantage. Comparisons to standard instruments were made in eight studies and were generally favourable. While there are many specialised instruments for endoscopic and endoscope-assisted neurosurgery available, the evidence for their safety, efficacy and usability is limited with non-standardised reporting and few comparative studies to standard instruments. Future innovation should be tailored to unmet clinical needs, and evaluation guided by structured development processes