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

Cellular infiltration in traumatic brain injury

Abstract: Traumatic brain injury leads to cellular damage which in turn results in the rapid release of damage-associated molecular patterns (DAMPs) that prompt resident cells to release cytokines and chemokines. These in turn rapidly recruit neutrophils, which assist in limiting the spread of injury and removing cellular debris. Microglia continuously survey the CNS (central nervous system) compartment and identify structural abnormalities in neurons contributing to the response. After some days, when neutrophil numbers start to decline, activated microglia and astrocytes assemble at the injury site—segregating injured tissue from healthy tissue and facilitating restorative processes. Monocytes infiltrate the injury site to produce chemokines that recruit astrocytes which successively extend their processes towards monocytes during the recovery phase. In this fashion, monocytes infiltration serves to help repair the injured brain. Neurons and astrocytes also moderate brain inflammation via downregulation of cytotoxic inflammation. Depending on the severity of the brain injury, T and B cells can also be recruited to the brain pathology sites at later time points

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

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

We Choose to Call it 'Degenerative Cervical Myelopathy': Findings of AO Spine RECODE-DCM, an International and Multi-Stakeholder Partnership to Agree a Standard Unifying Term and Definition for a Disease

STUDY DESIGN Modified DELPHI Consensus Process. OBJECTIVE To agree a single unifying term and definition. Globally, cervical myelopathy caused by degenerative changes to the spine is known by over 11 different names. This inconsistency contributes to many clinical and research challenges, including a lack of awareness. METHOD AO Spine RECODE-DCM (Research objectives and Common Data Elements Degenerative Cervical Myelopathy). To determine the index term, a longlist of candidate terms and their rationale, was created using a literature review and interviews. This was shared with the community, to select their preferred terms (248 members (58%) including 149 (60%) surgeons, 45 (18%) other healthcare professionals and 54 (22%) People with DCM or their supporters) and finalized using a consensus meeting. To determine a definition, a medical definition framework was created using inductive thematic analysis of selected International Classification of Disease definitions. Separately, stakeholders submitted their suggested definition which also underwent inductive thematic analysis (317 members (76%), 190 (59%) surgeons, 62 (20%) other healthcare professionals and 72 (23%) persons living with DCM or their supporters). Using this definition framework, a working definition was created based on submitted content, and finalized using consensus meetings. RESULTS Degenerative Cervical Myelopathy was selected as the unifying term, defined in short, as a progressive spinal cord injury caused by narrowing of the cervical spinal canal. CONCLUSION A consistent term and definition can support education and research initiatives. This was selected using a structured and iterative methodology, which may serve as an exemplar for others in the future

Comparative Learning Curves of Microscope Versus Exoscope: A Preclinical Randomized Crossover Noninferiority Study

BACKGROUND: An exoscope heralds a new era of optics in surgery. However, there is limited quantitative evidence describing and comparing the learning curve. OBJECTIVE: This study aimed to investigate the learning curve, plateau, and rate of novice surgeons using an Olympus ORBEYE exoscope compared to an operating microscope (Carl Zeiss OPMI PENTERO or KINEVO 900). METHODS: A preclinical, randomized, crossover, noninferiority trial assessed the performance of seventeen novice and seven expert surgeons completing the microsurgical grape dissection task “Star’s the limit.” A standardized star was drawn on a grape using a stencil with a 5 mm edge length. Participants cut the star and peeled the star-shaped skin off the grape with microscissors and forceps while minimizing damage to the grape flesh. Participants repeated the task 20 times consecutively for each optical device. Learning was assessed using model functions such as the Weibull function, and the cognitive workload was assessed with the NASA Task Load Index (NASA-TLX). RESULTS: Seventeen novice (male:female 12:5; median years of training 0.4 [0–2.8 years]) and six expert (male:female 4:2; median years of training 10 [8.9–24 years]) surgeons were recruited. “Star’s the limit” was validated using a performance score that gave a threshold of expert performance of 70 (0–100). The learning rate (ORBEYE −0.94 ± 0.37; microscope −1.30 ± 0.46) and learning plateau (ORBEYE 64.89 ± 8.81; microscope 65.93 ± 9.44) of the ORBEYE were significantly noninferior compared to those of the microscope group (p = 0.009; p = 0.027, respectively). The cognitive workload on NASA-TLX was higher for the ORBEYE. Novices preferred the freedom of movement and ergonomics of the ORBEYE but preferred the visualization of the microscope. CONCLUSIONS: This is the first study to quantify the ORBEYE learning curve and the first randomized controlled trial to compare the ORBEYE learning curve to that of the microscope. The plateau performance and learning rate of the ORBEYE are significantly noninferior to those of the microscope in a preclinical grape dissection task. This study also supports the ergonomics of the ORBEYE as reported in preliminary observational studies and highlights visualization as a focus for further development

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

RE-CODE DCM (search Objectives and ommon ata lements for egenerative ervical yelopathy): A Consensus Process to Improve Research Efficiency in DCM, Through Establishment of a Standardized Dataset for Clinical Research and the Definition of the Research Priorities

Study Design ixed-method consensus process. Objectives egenerative cervical myelopathy (DCM) is a common and disabling condition that arises when mechanical stress damages the spinal cord as a result of degenerative changes in the surrounding spinal structures. RECODE-DCM (search Objectives and ommon ata lements for egenerative ervical yelopathy) aims to improve efficient use of health care resources within the field of DCM by using a multi-stakeholder partnership to define the DCM research priorities, to develop a minimum dataset for DCM clinical studies, and confirm a definition of DCM. Methods his requires a multi-stakeholder partnership and multiple parallel consensus development processes. It will be conducted via 4 phases, adhering to the guidance set out by the COMET (Core Outcomes in Effectiveness Trials) and JLA (James Lind Alliance) initiatives. Phase 1 will consist of preliminary work to inform online Delphi processes (Phase 2) and a consensus meeting (Phase 3). Following the findings of the consensus meeting, a synthesis of relevant measurement instruments will be compiled and assessed as per the COSMIN (Consensus-based Standards for the Selection of Health Measurement Instruments) criteria, to allow recommendations to be made on how to measure agreed data points. Phase 4 will monitor and promote the use of eventual recommendations. Conclusions ECODE-DCM sets out to establish for the first time an index term, minimum dataset, and research priorities together. Our aim is to reduce waste of health care resources in the future by using patient priorities to inform the scope of future DCM research activities. The consistent use of a standard dataset in DCM clinical studies, audit, and clinical surveillance will facilitate pooled analysis of future data and, ultimately, a deeper understanding of DCM

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. Sixty-eight suitable studies were identified and qualitatively analysed. The risk of bias was evaluated using the Prediction model Risk of Bias Assessment Tool (PROBAST) criteria. All the studies were retrospective and preclinical. Gliomas were the most frequently analysed tumour type. The majority of studies used convolutional neural networks or support vector machines, and the most common goal of the model was for tumour classification and/or grading from haematoxylin and eosin-stained slides. The majority of studies were conducted when legacy World Health Organisation (WHO) classifications were in place, which at the time relied predominantly on histological (morphological) features but have since been superseded by molecular advances. Overall, there was a high risk of bias in all studies analysed. Persistent issues included inadequate transparency in reporting the number of patients and/or images within the model development and testing cohorts, absence of external validation, and insufficient recognition of batch effects in multi-institutional datasets. Based on these findings, we outline practical recommendations for future work including a framework for clinical implementation, in particular, better informing the artificial intelligence community of the needs of the neuropathologist

Robotic Handle Prototypes for Endoscopic Endonasal Skull Base Surgery: Pre-clinical Randomised Controlled Trial of Performance and Ergonomics

Endoscopic endonasal skull base surgery is a promising alternative to transcranial approaches. However, standard instruments lack articulation, and thus, could benefit from robotic technologies. The aim of this study was to develop an ergonomic handle for a handheld robotic instrument intended to enhance this procedure. Two different prototypes were developed based on ergonomic guidelines within the literature. The first is a forearm-mounted handle that maps the surgeon’s wrist degrees-of-freedom to that of the robotic end-effector; the second is a joystick-and-trigger handle with a rotating body that places the joystick to the position most comfortable for the surgeon. These handles were incorporated into a custom-designed surgical virtual simulator and were assessed for their performance and ergonomics when compared with a standard neurosurgical grasper. The virtual task was performed by nine novices with all three devices as part of a randomised crossover user-study. Their performance and ergonomics were evaluated both subjectively by themselves and objectively by a validated observational checklist. Both handles outperformed the standard instrument with the rotating joystick-body handle offering the most substantial improvement in terms of balance between performance and ergonomics. Thus, it is deemed the more suitable device to drive instrumentation for endoscopic endonasal skull base surgery