A minimum data set-Core outcome set, core data elements, and core measurement set-For degenerative cervical myelopathy research (AO Spine RECODE DCM): A consensus study.

BACKGROUND: Degenerative cervical myelopathy (DCM) is a progressive chronic spinal cord injury estimated to affect 1 in 50 adults. Without standardised guidance, clinical research studies have selected outcomes at their discretion, often underrepresenting the disease and limiting comparability between studies. Utilising a standard minimum data set formed via multi-stakeholder consensus can address these issues. This combines processes to define a core outcome set (COS)-a list of key outcomes-and core data elements (CDEs), a list of key sampling characteristics required to interpret the outcomes. Further "how" these outcomes should be measured and/or reported is then defined in a core measurement set (CMS). This can include a recommendation of a standardised time point at which outcome data should be reported. This study defines a COS, CDE, and CMS for DCM research. METHODS AND FINDINGS: A minimum data set was developed using a series of modified Delphi processes. Phase 1 involved the setup of an international DCM stakeholder group. Phase 2 involved the development of a longlist of outcomes, data elements, and formation into domains. Phase 3 prioritised the outcomes and CDEs using a two-stage Delphi process. Phase 4 determined the final DCM minimal data set using a consensus meeting. Using the COS, Phase 5 finalised definitions of the measurement construct for each outcome. In Phase 6, a systematic review of the literature was performed, to scope and define the psychometric properties of measurement tools. Phase 7 used a modified Delphi process to inform the short-listing of candidate measurement tools. The final measurement set was then formed through a consensus meeting (Phase 8). To support implementation, the data set was then integrated into template clinical research forms (CRFs) for use in future clinical trials (Phase 9). In total, 28 outcomes and 6 domains (Pain, Neurological Function, Life Impact, Radiology, Economic Impact, and Adverse Events) were entered into the final COS. Thirty two outcomes and 4 domains (Individual, Disease, Investigation, and Intervention) were entered into the final CDE. Finally, 4 outcome instruments (mJOA, NDI, SF-36v2, and SAVES2) were identified for the CMS, with a recommendation for trials evaluating outcomes after surgery, to include baseline measurement and at 6 months from surgery. CONCLUSIONS: The AO Spine RECODE-DCM has produced a minimum data set for use in DCM clinical trials today. These are available at https://myelopathy.org/minimum-dataset/. While it is anticipated the CDE and COS have strong and durable relevance, it is acknowledged that new measurement tools, alongside an increasing transition to study patients not undergoing surgery, may necessitate updates and adaptation, particularly with respect to the CMS.

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. PURPOSE: (1) Assess the validity and fidelity of a surgical simulator; (2) Examine the quantitative relationship between simulation performance and markers of real-world ability; (3) Establish thresholds for surgical expertise, and estimate their external validity and accuracy. STUDY DESIGN/SETTING: A cohort study of surgeons at a British neurosurgical center. STUDY SAMPLE: 10 early-career "novice" surgeons and 8 board-certified "expert" neurosurgeons. OUTCOMES MEASURES: (1) Face and content validity, and visual and haptic fidelity; (2) Construct validity; (3) Predictive and discriminative utility of quantitative performance thresholds. METHODS: Participants performed unilateral lumbar decompressions on high-fidelity spinal simulators that replicate the bony and soft tissue anatomy along with physiological processes such as bleeding and CSF leaks. Operating times, measured from first surgical action to either self-perceived satisfactory decompression or the end of allocated time, were recorded. The performance was also assessed independently by two blinded spinal subspecialist neurosurgeons using OSATS, a validated surgical assessment tool that utilizes five-point scales on a variety of technical domains to grade the overall technical proficiency. Validity and fidelity were assessed by expert neurosurgeons using quantitative questionnaires. Construct validity was assessed by ordinal regression of simulation performance against real-world surgical grade and portfolio. Thresholds of expert status by simulation performance was established, and their predictive and discriminative utility assessed by crossvalidation accuracy and AUC-ROC. RESULTS: Operating time and expert assessments of simulation performance (OSATS) were strong and significant predictors of surrogate markers of real-world surgical ability. The thresholds for expert status were operating time of 15 minutes and modified OSATS score of 15/20. These thresholds predicted expert status with 84.2% and 71.4% accuracy respectively. Strong discriminative ability was demonstrated by AUC-ROC of 0.95 and 0.83 respectively. All expert surgeons agreed that RealSpine simulators demonstrate high face validity, and high visual and haptic fidelity, with overall scores showing statistically significant agreement on these items (all scores at least 4/5, P<.0001). There was less consensus on content validity, but with still significant overall agreement (average score: 3.75/5, P=0.023). CONCLUSIONS: Real-world surgical ability and experience can be accurately predicted by defining objective quantitative thresholds on high-fidelity simulations. The thresholds established here, along with other data presented in this paper, may inform objectives and standards to be established in a spinal surgical training curriculum.

Hantavirus-Induced Cardiac Tamponade: A Diagnostic Dilemma.

Herein we describe a case of a 60-year-old white male from New York City who was admitted to hospital due to worsening dyspnea. He presented with an acute onset of fever, night sweats, and progressively worsening non-productive cough and orthopnea over the preceding week. Electrocardiogram findings revealed atrial fibrillation. Manifesting signs of hypoperfusion, a trans-esophageal echocardiography was performed, which demonstrated the presence of a cardiac tamponade. An emergency pericardiocentesis was performed, draining 750 cc of serosanguinous content. Laboratory investigations depicted an inflammatory milieu marked by lymphocytic leukocytosis, cardiac function impairment, and remarkably elevated d-dimer and brain natriuretic peptide levels. Notably, high-sensitivity troponin T remained within normal limits. Comprehensive viral panel assays, including COVID-19, Influenza A+B, Respiratory Syncytial Virus, Hepatitis C, HIV, Cytomegalovirus, Coxsackie A+B, and Herpes Simplex Virus, returned negative results. Furthermore, anti-nuclear factor and rheumatoid factor titers were negative. Blood and fungal cultures, as well as assessments for Mycobacterium tuberculosis, yielded negative findings. On further history-taking, he reported that he had occupational exposure to rat droppings and urine two weeks ago. Serological analysis demonstrated positive hantavirus IgG and IgM antibodies. Supportive management was initiated. Consequently, the patient was discharged asymptomatic, without pericardial effusion. Evaluation after two weeks revealed no recurrence of symptoms.

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. METHODS: Endoscopic pituitary adenoma surgery videos from a single center were annotated by experts for surgical workflow (3 phases, 15 steps) and surgical skill (using modified Objective Structured Assessment of Technical Skills; mOSATS). Quantitative workflow metrics were calculated, including phase duration and step transitions. Poisson or logistic regression was used to assess the association of workflow metrics and mOSATS with common inpatient surgical outcomes. RESULTS: 100 videos from 100 patients were included. Nasal phase mean duration was 24mins and mean mOSATS was 21.2/30. Mean duration was 34mins and mean mOSATS was 20.9/30 for the sellar phase, and 11mins and 21.7/30 respectively for the closure phase. The most common adverse outcomes were new anterior pituitary hormone deficiency (n=26), dysnatremia (n=24) and cerebrospinal fluid (CSF) leak (n=5). Higher mOSATS for all three phases and shorter operation duration was associated with decreased length of stay (p=0.003 & p<0.001). Superior closure phase mOSATS were associated with reduced post-operative CSF leak (p<0.001), and superior sellar phase mOSATS were associated with reduced post-operative visual deterioration (p=0.041). CONCLUSION: Superior surgical skill and shorter surgical time were associated with superior surgical outcomes, at a generic and phase-specific level. Such video-based analysis has promise for integration into data-driven training and service improvement initiatives.

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). METHODS: AI-assisted video-based surgical coaching was implemented over 6 months with the pituitary surgery team. The program consisted of 1) monthly random video analysis and review; and 2) quarterly 2-hour educational meetings discussing these videos and learning points. Each video was annotated for surgical phases and steps using AI, which improved video interactivity and allowed calculation of quantitative metrics. Primary outcomes were program feasibility, acceptability and appropriateness. Surgical performance (via modified Objective Structured Assessment of Technical Skills; mOSATS) and early surgical outcomes were recorded for every case during the 6-month coaching period, and a preceding 6-month control period. Beta and logistic regression were used to assess the change in mOSATS and surgical outcomes after the coaching program implementation. RESULTS: All participants highly rated the program's feasibility, acceptability and appropriateness. During the coaching program, 63 endoscopic pituitary adenoma cases were included, with 41 in the control group. Surgical performance across all operative phases improved during the coaching period (p<0.001), with a reduction in new post-operative anterior pituitary hormone deficit (p=0.01). CONCLUSION: We have developed a novel AI-assisted video surgical coaching program for endoscopic pituitary adenoma surgery - demonstrating its viability and impact on surgical performance. Early results also suggest improvement in patient outcomes. Future studies should be multicenter and longer term.

A histopathological and epidemiological study of urothelial carcinoma at a tertiary care centre in Peshawar, Pakistan.

Bladder cancer is the ninth leading cause of death worldwide and 14th leading cause of death in Pakistan. The objective of this study was to determine the frequency of urothelial carcinoma in various age groups, its gender distribution, and grades. A total of 131 cases of urothelial carcinoma, received at Department of Pathology, Peshawar Medical College, Peshawar, between January 2017 to December 2022, were included in the study; of them 107 (81.6%) were males while 24 (18.3%) were females with a mean age of 62±13 years. The most common histological subtype was papillary urothelial carcinoma in 117(89.3%) cases, followed by Squamous and Glandular in 5(3.8%) cases. Majority of the urothelial carcinoma with high grade showed a statistically significant relation with muscle invasion 38 (50.66%). Males were four times more likely to have urothelial carcinoma while older age groups were more likely to have high grade urothelial carcinoma.

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. We assessed the impact of a two day (non-credit bearing) online digital literacy course on the ability of individuals to critically appraise AI literature in neurosurgery. METHODS: We performed a prospective, quasi-experimental non-randomised, controlled study with an intervention arm comprised of individuals enrolled in our two-day digital health literacy course and a waiting-list control arm used for comparison. We assessed participants' pre- and post-course knowledge, confidence and course acceptability using Qualtrics surveys designed for the purpose of this study. RESULTS: A total of 62 participants (33 participants, 29 waitlist controls), including neurosurgical trainees and both undergraduate and post-graduate students, attended the course and completed the pre-course survey. The two groups did not vary significantly in terms of age or demographics. Following the course, participants significantly improved in their knowledge of AI (mean difference=3.86, 95% CI=2.97-4.75, p-value<0.0001) and confidence in critically appraising literature using AI (p-value=0.002). Similar differences in knowledge (mean difference=3.15, 95% CI=1.82-4.47, p-value<0.0001) and confidence (p-value<0.0001) were found when compared to the control group. CONCLUSION: Bespoke courses delivered at an appropriate level can improve clinicians' understanding of the application of AI in neurosurgery, without the need for in-depth technical knowledge or programming skills.

Handheld robotic device for endoscopic neurosurgery: system integration and pre-clinical evaluation.

The Expanded Endoscopic Endonasal Approach, one of the best examples of endoscopic neurosurgery, allows access to the skull base through the natural orifice of the nostril. Current standard instruments lack articulation limiting operative access and surgeon dexterity, and thus, could benefit from robotic articulation. In this study, a handheld robotic system with a series of detachable end-effectors for this approach is presented. This system is comprised of interchangeable articulated 2/3 degrees-of-freedom 3 mm instruments that expand the operative workspace and enhance the surgeon's dexterity, an ergonomically designed handheld controller with a rotating joystick-body that can be placed at the position most comfortable for the user, and the accompanying control box. The robotic instruments were experimentally evaluated for their workspace, structural integrity, and force-delivery capabilities. The entire system was then tested in a pre-clinical context during a phantom feasibility test, followed up by a cadaveric pilot study by a cohort of surgeons of varied clinical experience. Results from this series of experiments suggested enhanced dexterity and adequate robustness that could be associated with feasibility in a clinical context, as well as improvement over current neurosurgical instruments.

Complexities of Infective Endocarditis in a Young Patient With Hypertrophic Obstructive Cardiomyopathy: A Case of Dual Valve Involvement and Neurological Complications.

Infective endocarditis (IE) is a life-threatening infection predominantly affecting the endocardium and heart valves, commonly seen in older patients and those with pre-existing cardiac conditions. Although rare in younger individuals with intact cardiac valves, certain structural heart diseases such as hypertrophic obstructive cardiomyopathy (HOCM) can increase the risk. We present a unique case of a 39-year-old female with a known history of HOCM, a condition characterized by abnormally thickened cardiac muscle primarily affecting the left ventricle. This patient was diagnosed with group B streptococcus infective endocarditis. Notably, this case was complicated by septic emboli to the brain. This case underscores the significant risk of IE in patients with HOCM, a demographic usually less susceptible to IE. It underscores the importance of early recognition and aggressive management of IE, especially in patients with structural heart diseases.

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.

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization.

The domain of human locomotion identification through smartphone sensors is witnessing rapid expansion within the realm of research. This domain boasts significant potential across various sectors, including healthcare, sports, security systems, home automation, and real-time location tracking. Despite the considerable volume of existing research, the greater portion of it has primarily concentrated on locomotion activities. Comparatively less emphasis has been placed on the recognition of human localization patterns. In the current study, we introduce a system by facilitating the recognition of both human physical and location-based patterns. This system utilizes the capabilities of smartphone sensors to achieve its objectives. Our goal is to develop a system that can accurately identify different human physical and localization activities, such as walking, running, jumping, indoor, and outdoor activities. To achieve this, we perform preprocessing on the raw sensor data using a Butterworth filter for inertial sensors and a Median Filter for Global Positioning System (GPS) and then applying Hamming windowing techniques to segment the filtered data. We then extract features from the raw inertial and GPS sensors and select relevant features using the variance threshold feature selection method. The extrasensory dataset exhibits an imbalanced number of samples for certain activities. To address this issue, the permutation-based data augmentation technique is employed. The augmented features are optimized using the Yeo-Johnson power transformation algorithm before being sent to a multi-layer perceptron for classification. We evaluate our system using the K-fold cross-validation technique. The datasets used in this study are the Extrasensory and Sussex Huawei Locomotion (SHL), which contain both physical and localization activities. Our experiments demonstrate that our system achieves high accuracy with 96% and 94% over Extrasensory and SHL in physical activities and 94% and 91% over Extrasensory and SHL in the location-based activities, outperforming previous state-of-the-art methods in recognizing both types of activities.

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. We utilized MedCAT, a named entity recognition and linking NLP, integrated into the electronic health record (EHR), which extracts concepts and links them to systematized nomenclature of medicine-clinical terms (SNOMED-CT). Investigators reviewed clinic letters, identifying words or phrases that described or identified operations and recording the SNOMED-CT terms as ground truth. This was compared to SNOMED-CT terms identified by the model, untrained on our dataset. A pipeline linking clinic letters to patient-specific educational resources was established, and precision, recall, and F1 scores were calculated. RESULTS: Across 199 letters the model identified 582 surgical procedures, and the overall pipeline after adding rules a total of 784 procedures (precision = 0.94, recall = 0.86, F1 = 0.91). Across 187 letters with identified SNOMED-CT terms the integrated pipeline linking education resources directly to the EHR was successful in 157 (78%) patients (precision = 0.99, recall = 0.87, F1 = 0.92). CONCLUSIONS: NLP accurately identifies surgical procedures in pre-operative clinic letters within an untrained subspecialty. Performance varies among letter authors and depends on the language used by clinicians. The identified procedures can be linked to patient education resources, potentially improving patients' understanding of surgical procedures.

A Case of acute Myocardial Infarction in a Patient with Essential Thrombocythaemia Treated with Anagrelide.

Anagrelide is a medication primarily used to manage thrombocytosis, an abnormal increase in platelet levels in the blood. It is often prescribed for patients with myeloproliferative disorders, such as essential thrombocythaemia (ET). Given the heightened susceptibility to thromboembolism associated with this condition, the primary emphasis in treatment revolves around reducing the risk of thrombotic events through the administration of cytotoxic agents. While anagrelide is generally effective in reducing platelet counts, it comes with potential side effects, including an increased risk of certain thrombotic events. Anagrelide acts by inhibiting megakaryocyte maturation and platelet release, thereby reducing platelet production. However, this platelet-lowering effect may be accompanied by an increase in platelet activation and reactivity, which could contribute to a prothrombotic state. We present a case of a 60-year-old female with a history of ET, managed with anagrelide and hydroxyurea therapy, who experienced an acute ST-elevation myocardial infarction. LEARNING POINTS: The dual role of anagrelide: although anagrelide is effective in lowering platelet levels in essential thrombocythaemia, it can increase platelet activation, raising thrombotic risk. Clinicians need to monitor patients closely for thrombotic events.Balancing efficacy and side effects: the risk of severe side effects such as myocardial infarction, as seen in this case report, necessitates a balanced approach in using anagrelide, weighing its benefits against potential risks.

Robust human locomotion and localization activity recognition over multisensory.

Human activity recognition (HAR) plays a pivotal role in various domains, including healthcare, sports, robotics, and security. With the growing popularity of wearable devices, particularly Inertial Measurement Units (IMUs) and Ambient sensors, researchers and engineers have sought to take advantage of these advances to accurately and efficiently detect and classify human activities. This research paper presents an advanced methodology for human activity and localization recognition, utilizing smartphone IMU, Ambient, GPS, and Audio sensor data from two public benchmark datasets: the Opportunity dataset and the Extrasensory dataset. The Opportunity dataset was collected from 12 subjects participating in a range of daily activities, and it captures data from various body-worn and object-associated sensors. The Extrasensory dataset features data from 60 participants, including thousands of data samples from smartphone and smartwatch sensors, labeled with a wide array of human activities. Our study incorporates novel feature extraction techniques for signal, GPS, and audio sensor data. Specifically, for localization, GPS, audio, and IMU sensors are utilized, while IMU and Ambient sensors are employed for locomotion activity recognition. To achieve accurate activity classification, state-of-the-art deep learning techniques, such as convolutional neural networks (CNN) and long short-term memory (LSTM), have been explored. For indoor/outdoor activities, CNNs are applied, while LSTMs are utilized for locomotion activity recognition. The proposed system has been evaluated using the k-fold cross-validation method, achieving accuracy rates of 97% and 89% for locomotion activity over the Opportunity and Extrasensory datasets, respectively, and 96% for indoor/outdoor activity over the Extrasensory dataset. These results highlight the efficiency of our methodology in accurately detecting various human activities, showing its potential for real-world applications. Moreover, the research paper introduces a hybrid system that combines machine learning and deep learning features, enhancing activity recognition performance by leveraging the strengths of both approaches.

Successful treatment of dumping syndrome with diazoxide in an infant with hypoplastic left heart syndrome.

Summary: Dumping syndrome is a rare but potentially serious condition that causes inappropriate postprandial hyperinsulinemia leading to hypoglycemia in children following gastrointestinal surgeries. While dietary modifications are often the first line of treatment, severe cases may require pharmacological intervention to prevent severe hypoglycemia. We present a case of successful treatment of dumping syndrome with diazoxide. A 2-month-old infant with left hypoplastic heart syndrome who underwent single ventricle palliation pathway and developed feeding intolerance that required Nissen fundoplication. Postprandial hypoglycemia was detected following the procedure, with glucose level down to 12 mg/dL, and the diagnosis of dumping syndrome was established. The patient was successfully managed with diazoxide, which effectively resolved postprandial hypoglycemia without any major adverse events. The patient was eventfully weaned off the medication at the age of 5 months. This case highlights the potential role of diazoxide in the management of pediatric patients with postprandial hyperinsulinemic hypoglycemia secondary to dumping syndrome. Learning points: Dumping syndrome is a possible complication of gastrointestinal surgeries and should be suspected in children with abnormal glucose levels. Postprandial hyperglycemia should be monitored closely for significant subsequent hypoglycemia. Diazoxide might be considered as part of the treatment plan for dumping syndrome.

Distribution of case volumes in surgery: an analysis of the British Spine Registry.

Objectives: To characterize the distribution of case volumes within a surgical field. Design: An analysis of British Spine Registry. Setting: 295 centers in England that conducted at least one spinal operation either within the NHS or private settings between 1 May 2016 and 27 February 2021. Participants: 644 surgeons. Main outcome measures: Mathematical descriptions of distributions of cases among surgeons and the extent of workforce-level case-volume concentration as a surrogate marker. Results: There were wide variations in monthly caseloads between surgeons, ranging from 0 to average monthly high of 81.8 cases. The curves showed that 37.7% of surgeons were required to perform 80% of all spinal operations, which is substantially less than in fields outside of healthcare.With the COVID-19 pandemic, the case volumes of surgeons with the highest volumes dropped dramatically, whereas those with the lowest case numbers remained nearly unchanged. This, along with the relatively low level of case-volume concentration within spinal surgery, may indicate an inevitability of at least some level of surgical care being provided by the relatively lower volume surgeons. Conclusions: While there is a reasonable degree of workforce-level case volume concentration within spinal surgery, with high volume spinal surgeons providing a large proportion of care, it is not clear whether a further concentration of case volumes into those few hands is possible or desirable.

PitSurgRT: real-time localization of critical anatomical structures in endoscopic pituitary surgery.

PURPOSE: Endoscopic pituitary surgery entails navigating through the nasal cavity and sphenoid sinus to access the sella using an endoscope. This procedure is intricate due to the proximity of crucial anatomical structures (e.g. carotid arteries and optic nerves) to pituitary tumours, and any unintended damage can lead to severe complications including blindness and death. Intraoperative guidance during this surgery could support improved localization of the critical structures leading to reducing the risk of complications. METHODS: A deep learning network PitSurgRT is proposed for real-time localization of critical structures in endoscopic pituitary surgery. The network uses high-resolution net (HRNet) as a backbone with a multi-head for jointly localizing critical anatomical structures while segmenting larger structures simultaneously. Moreover, the trained model is optimized and accelerated by using TensorRT. Finally, the model predictions are shown to neurosurgeons, to test their guidance capabilities. RESULTS: Compared with the state-of-the-art method, our model significantly reduces the mean error in landmark detection of the critical structures from 138.76 to 54.40 pixels in a 1280 × 720-pixel image. Furthermore, the semantic segmentation of the most critical structure, sella, is improved by 4.39% IoU. The inference speed of the accelerated model achieves 298 frames per second with floating-point-16 precision. In the study of 15 neurosurgeons, 88.67% of predictions are considered accurate enough for real-time guidance. CONCLUSION: The results from the quantitative evaluation, real-time acceleration, and neurosurgeon study demonstrate the proposed method is highly promising in providing real-time intraoperative guidance of the critical anatomical structures in endoscopic pituitary surgery.

A Wearable Inertial Sensor Approach for Locomotion and Localization Recognition on Physical Activity.

Advancements in sensing technology have expanded the capabilities of both wearable devices and smartphones, which are now commonly equipped with inertial sensors such as accelerometers and gyroscopes. Initially, these sensors were used for device feature advancement, but now, they can be used for a variety of applications. Human activity recognition (HAR) is an interesting research area that can be used for many applications like health monitoring, sports, fitness, medical purposes, etc. In this research, we designed an advanced system that recognizes different human locomotion and localization activities. The data were collected from raw sensors that contain noise. In the first step, we detail our noise removal process, which employs a Chebyshev type 1 filter to clean the raw sensor data, and then the signal is segmented by utilizing Hamming windows. After that, features were extracted for different sensors. To select the best feature for the system, the recursive feature elimination method was used. We then used SMOTE data augmentation techniques to solve the imbalanced nature of the Extrasensory dataset. Finally, the augmented and balanced data were sent to a long short-term memory (LSTM) deep learning classifier for classification. The datasets used in this research were Real-World Har, Real-Life Har, and Extrasensory. The presented system achieved 89% for Real-Life Har, 85% for Real-World Har, and 95% for the Extrasensory dataset. The proposed system outperforms the available state-of-the-art methods.

Patient-specific automated cerebrospinal fluid pressure control to augment spinal wound closure: a case series using the LiquoGuard®.

OBJECTIVE: Spinal cerebrospinal fluid (CSF) leaks are common, and their management is heterogeneous. For high-flow leaks, numerous studies advocate for primary dural repair and CSF diversion. The LiquoGuard7® allows automated and precise pressure and volume control, and calculation of patient-specific CSF production rate (prCSF), which is hypothesized to be increased in the context of durotomies and CSF leaks. METHODS: This single-centre illustrative case series included patients undergoing complex spinal surgery where: 1) a high flow intra-operative and/or post-operative CSF leak was expected and 2) lumbar CSF drainage was performed using a LiquoGuard7®. CSF diversion was tailored to prCSF for each patient, combined with layered spinal wound closure. RESULTS: Three patients were included, with a variety of pathologies: T7/T8 disc prolapse, T8-T9 meningioma, and T4-T5 metastatic spinal cord compression. The first two patients underwent CSF diversion to prevent post-op CSF leak, whilst the third required this in response to post-op CSF leak. CSF hyperproduction was evident in all cases (mean >/=140ml/hr). With patient-specific CSF diversion regimes, no cases required further intervention for CSF fistulae repair (including for pleural CSF effusion), wound breakdown or infection. CONCLUSIONS: Patient-specific cerebrospinal fluid drainage may be a useful tool in the management of high-flow intra-operative and post-operative CSF leaks during complex spinal surgery. These systems may reduce post-operative CSF leakage from the wound or into adjacent body cavities. Further larger studies are needed to evaluate the comparative benefits and cost-effectiveness of this approach.