Automatic generation of operation notes in endoscopic pituitary surgery videos using workflow recognition

Operation notes are a crucial component of patient care. However, writing them manually is prone to human error, particularly in high pressured clinical environments. Automatic generation of operation notes from video recordings can alleviate some of the administrative burdens, improve accuracy, and provide additional information. To achieve this for endoscopic pituitary surgery, 27-steps were identified via expert consensus. Then, for the 97-videos recorded for this study, a timestamp of each step was annotated by an expert surgeon. To automatically determine whether a step is present in a video, a three-stage architecture was created. Firstly, for each step, a convolution neural network was used for binary image classification on each frame of a video. Secondly, for each step, the binary frame classifications were passed to a discriminator for binary video classification. Thirdly, for each video, the binary video classifications were passed to an accumulator for multi-label step classification. The architecture was trained on 77-videos, and tested on 20-videos, where a 0.80 weighted-F1 score was achieved. The classifications were inputted into a clinically based predefined template, and further enriched with additional video analytics. This work therefore demonstrates automatic generation of operative notes from surgical videos is feasible, and can assist surgeons during documentation

Historical and future trends in emergency pituitary referrals: a machine learning analysis

Purpose: Acute pituitary referrals to neurosurgical services frequently necessitate emergency care. Yet, a detailed characterisation of pituitary emergency referral patterns, including how they may change prospectively is lacking. This study aims to evaluate historical and current pituitary referral patterns and utilise state-of-the-art machine learning tools to predict future service use. Methods: A data-driven analysis was performed using all available electronic neurosurgical referrals (2014–2021) to the busiest U.K. pituitary centre. Pituitary referrals were characterised and volumes were predicted using an auto-regressive moving average model with a preceding seasonal and trend decomposition using Loess step (STL-ARIMA), compared against a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) algorithm, Prophet and two standard baseline forecasting models. Median absolute, and median percentage error scoring metrics with cross-validation were employed to evaluate algorithm performance. Results: 462 of 36,224 emergency referrals were included (referring centres = 48; mean patient age = 56.7 years, female:male = 0.49:0.51). Emergency medicine and endocrinology accounted for the majority of referrals (67%). The most common presentations were headache (47%) and visual field deficits (32%). Lesions mainly comprised tumours or haemorrhage (85%) and involved the pituitary gland or fossa (70%). The STL-ARIMA pipeline outperformed CNN-LSTM, Prophet and baseline algorithms across scoring metrics, with standard accuracy being achieved for yearly predictions. Referral volumes significantly increased from the start of data collection with future projected increases (p < 0.001) and did not significantly reduce during the COVID-19 pandemic. Conclusion: This work is the first to employ large-scale data and machine learning to describe and predict acute pituitary referral volumes, estimate future service demands, explore the impact of system stressors (e.g. COVID pandemic), and highlight areas for service improvement

Artificial Intelligence in Brain Tumour Surgery—An Emerging Paradigm

Artificial intelligence (AI) platforms have the potential to cause a paradigm shift in brain tumour surgery. Brain tumour surgery augmented with AI can result in safer and more effective treatment. In this review article, we explore the current and future role of AI in patients undergoing brain tumour surgery, including aiding diagnosis, optimising the surgical plan, providing support during the operation, and better predicting the prognosis. Finally, we discuss barriers to the successful clinical implementation, the ethical concerns, and we provide our perspective on how the field could be advanced

Robotic Semi-Automated Transcranial Doppler Assessment of Cerebrovascular Autoregulation in Post Concussional Syndrome: Methodological Considerations

Abstract Introduction Post-concussive syndrome (PCS) refers to a constellation of physical, cognitive, and emotional symptoms after traumatic brain injury (TBI). Despite its incidence, the underlying mechanisms are unclear. We hypothesised that impaired cerebral autoregulation (CA) is a contributor. Method A prospective, observational study was integrated into outpatient clinics at a tertiary neurosurgical centre. Data points included: demographics, symptoms (Post-Concussion Symptom Scale [PCSS]), neuropsychological assessment (Cambridge Neuropsychological Test Automated-Battery [CANTAB]) and cerebrovascular metrics (Mxa co-efficient and the transient hyperaemic-response ratio [THRR]) - via transcranial Doppler (TCD), plethysmography and bespoke software (ICM+). Results 12 participants were recruited with 2 excluded after unsuccessful cerebrovascular TCD insonation. 10 participants (5 TBI patients, 5 healthy controls) were included in the analysis (median age 26.5, male:female 7:3). Median PCSS scores were 6/126 (TBI subgroup). Median CANTAB percentiles were 78 (healthy controls) and 25 (TBI). Mxa was calculated for 90% and THRR for 50% of participants. Median study time was 127.5 minutes and feedback (n = 6) highlighted the perceived acceptability of the study. Conclusions This pilot study has demonstrated a feasible and reproducible assessment of PCS and CA metrics (non-invasively) in a real-world setting. By scaling this methodology, we hope to test whether CA changes are correlated with symptomatic PCS in patients post-TBI. </jats:sec

Microsurgery for intracranial aneurysms: A qualitative survey on technical challenges and technological solutions

INTRODUCTION: Microsurgery for the clipping of intracranial aneurysms remains a technically challenging and high-risk area of neurosurgery. We aimed to describe the technical challenges of aneurysm surgery, and the scope for technological innovations to overcome these barriers from the perspective of practising neurovascular surgeons. MATERIALS AND METHODS: Consultant neurovascular surgeons and members of the British Neurovascular Group (BNVG) were electronically invited to participate in an online survey regarding surgery for both ruptured and unruptured aneurysms. The free text survey asked three questions: what do they consider to be the principal technical barriers to aneurysm clipping? What technological advances have previously contributed to improving the safety and efficacy of aneurysm clipping? What technological advances do they anticipate improving the safety and efficacy of aneurysm clipping in the future? A qualitative synthesis of responses was performed using multi-rater emergent thematic analysis. RESULTS: The most significant reported historical advances in aneurysm surgery fell into five themes: (1) optimising clip placement, (2) minimising brain retraction, (3) tissue handling, (4) visualisation and orientation, and (5) management of intraoperative rupture. The most frequently reported innovation by far was indocyanine green angiography (84% of respondents). The three most commonly cited future advances were hybrid surgical and endovascular techniques, advances in intraoperative imaging, and patient-specific simulation and planning. CONCLUSIONS: While some surgeons perceive that the rate of innovation in aneurysm clipping has been dwarfed in recent years by endovascular techniques, surgeons surveyed highlighted a broad range of future technologies that have the potential to continue to improve the safety of aneurysm surgery in the future

The clinical outcomes of imaging modalities for surgical management Cushing’s disease – A systematic review and meta-analysis

Introduction: Cushing’s disease presents major diagnostic and management challenges. Although numerous preoperative and intraoperative imaging modalities have been deployed, it is unclear whether these investigations have improved surgical outcomes. Our objective was to investigate whether advances in imaging improved outcomes for Cushing’s disease. Methods: Searches of PubMed and EMBASE were conducted. Studies reporting on imaging modalities and clinical outcomes after surgical management of Cushing’s disease were included. Multilevel multivariable meta-regressions identified predictors of outcomes, adjusting for confounders and heterogeneity prior to investigating the effects of imaging. Results: 166 non-controlled single-arm studies were included, comprising 13181 patients over 44 years. The overall remission rate was 77.0% [CI: 74.9%-79.0%]. Cavernous sinus invasion (OR: 0.21 [CI: 0.07-0.66]; p=0.010), radiologically undetectable lesions (OR: 0.50 [CI: 0.37–0.69]; p<0.0001), previous surgery (OR=0.48 [CI: 0.28–0.81]; p=0.008), and lesions ≥10mm (OR: 0.63 [CI: 0.35–1.14]; p=0.12) were associated with lower remission. Less stringent thresholds for remission was associated with higher reported remission (OR: 1.37 [CI: 1.1–1.72]; p=0.007). After adjusting for this heterogeneity, no imaging modality showed significant differences in remission compared to standard preoperative MRI. The overall recurrence rate was 14.5% [CI: 12.1%-17.1%]. Lesion ≥10mm was associated with greater recurrence (OR: 1.83 [CI: 1.13–2.96]; p=0.015), as was greater duration of follow-up (OR: 1.53 (CI: 1.17–2.01); p=0.002). No imaging modality was associated with significant differences in recurrence. Despite significant improvements in detection rates over four decades, there were no significant changes in the reported remission or recurrence rates. Conclusion: A lack of controlled comparative studies makes it difficult to draw definitive conclusions. Within this limitation, the results suggest that despite improvements in radiological detection rates of Cushing’s disease over the last four decades, there were no changes in clinical outcomes. Advances in imaging alone may be insufficient to improve surgical outcomes. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42020187751

Automated operative workflow analysis of endoscopic pituitary surgery using machine learning: development and preclinical evaluation (IDEAL stage 0)

OBJECTIVE: Surgical workflow analysis involves systematically breaking down operations into key phases and steps. Automatic analysis of this workflow has potential uses for surgical training, preoperative planning, and outcome prediction. Recent advances in machine learning (ML) and computer vision have allowed accurate automated workflow analysis of operative videos. In this Idea, Development, Exploration, Assessment, Long-term study (IDEAL) stage 0 study, the authors sought to use Touch Surgery for the development and validation of an ML-powered analysis of phases and steps in the endoscopic transsphenoidal approach (eTSA) for pituitary adenoma resection, a first for neurosurgery. METHODS: The surgical phases and steps of 50 anonymized eTSA operative videos were labeled by expert surgeons. Forty videos were used to train a combined convolutional and recurrent neural network model by Touch Surgery. Ten videos were used for model evaluation (accuracy, F1 score), comparing the phase and step recognition of surgeons to the automatic detection of the ML model. RESULTS: The longest phase was the sellar phase (median 28 minutes), followed by the nasal phase (median 22 minutes) and the closure phase (median 14 minutes). The longest steps were step 5 (tumor identification and excision, median 17 minutes); step 3 (posterior septectomy and removal of sphenoid septations, median 14 minutes); and step 4 (anterior sellar wall removal, median 10 minutes). There were substantial variations within the recorded procedures in terms of video appearances, step duration, and step order, with only 50% of videos containing all 7 steps performed sequentially in numerical order. Despite this, the model was able to output accurate recognition of surgical phases (91% accuracy, 90% F1 score) and steps (76% accuracy, 75% F1 score). CONCLUSIONS: In this IDEAL stage 0 study, ML techniques have been developed to automatically analyze operative videos of eTSA pituitary surgery. This technology has previously been shown to be acceptable to neurosurgical teams and patients. ML-based surgical workflow analysis has numerous potential uses-such as education (e.g., automatic indexing of contemporary operative videos for teaching), improved operative efficiency (e.g., orchestrating the entire surgical team to a common workflow), and improved patient outcomes (e.g., comparison of surgical techniques or early detection of adverse events). Future directions include the real-time integration of Touch Surgery into the live operative environment as an IDEAL stage 1 (first-in-human) study, and further development of underpinning ML models using larger data sets

Aphrodisiac activity of 50% ethanolic extracts of Myristica fragrans Houtt. (nutmeg) and Syzygium aromaticum (L) Merr. & Perry. (clove) in male mice: a comparative study

BACKGROUND: Spices are considered as sexual invigorators in the Unani System of Medicine. In order to explore the sexual function improving effect of Myristica fragrans Houtt. (nutmeg) and Syzygium aromaticum (L) Merr. & Perry. (clove) an experimental study was conducted in normal male mice. METHODS: The extracts (50% ethanolic) of nutmeg and clove were administered (500 mg/kg; p.o.) to different groups of male Swiss mice. Mounting behaviour, mating performance, and general short term toxicity of the test drugs were determined and compared with the standard drug Penegra (Sildenafil citrate). RESULTS: The extracts of the nutmeg and clove were found to stimulate the mounting behaviour of male mice, and also to significantly increase their mating performance. The drugs were devoid of any conspicuous general short term toxicity. CONCLUSION: The extracts (50% ethanolic) of nutmeg and clove enhanced the sexual behaviour of male mice

The association between histamine 2 receptor antagonist use and Clostridium difficile infection: a systematic review and meta-analysis.

Background Clostridium difficile infection (CDI) is a major health problem. Epidemiological evidence suggests that there is an association between acid suppression therapy and development of CDI. Purpose We sought to systematically review the literature that examined the association between histamine 2 receptor antagonists (H2RAs) and CDI. Data source We searched Medline, Current Contents, Embase, ISI Web of Science and Elsevier Scopus from 1990 to 2012 for all analytical studies that examined the association between H2RAs and CDI. Study selection Two authors independently reviewed the studies for eligibility. Data extraction Data about studies characteristics, adjusted effect estimates and quality were extracted. Data synthesis Thirty-five observations from 33 eligible studies that included 201834 participants were analyzed. Studies were performed in 6 countries and nine of them were multicenter. Most studies did not specify the type or duration of H2RAs therapy. The pooled effect estimate was 1.44, 95% CI (1.22–1.7), I2 = 70.5%. This association was consistent across different subgroups (by study design and country) and there was no evidence of publication bias. The pooled effect estimate for high quality studies was 1.39 (1.15–1.68), I2 = 72.3%. Meta-regression analysis of 10 study-level variables did not identify sources of heterogeneity. In a speculative analysis, the number needed to harm (NNH) with H2RAs at 14 days after hospital admission in patients receiving antibiotics or not was 58, 95% CI (37, 115) and 425, 95% CI (267, 848), respectively. For the general population, the NNH at 1 year was 4549, 95% CI (2860, 9097). Conclusion In this rigorous systematic review and meta-analysis, we observed an association between H2RAs and CDI. The absolute risk of CDI associated with H2RAs is highest in hospitalized patients receiving antibiotics