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The Nuffield Department of Surgical Sciences is the academic department of surgery at the University of Oxford, and hosts a multidisciplinary team of senior clinical academic surgeons, senior scientists, junior clinicians and scientists in training.
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.
Beyond the RCT: When are Randomized Trials Unnecessary for New Therapeutic Devices, and What Should We Do Instead?
OBJECTIVE: The aim of this study was to develop an evidence-based framework for evaluation of therapeutic devices, based on ethical principles and clinical evidence considerations. SUMMARY BACKGROUND DATA: Nearly all medical products which do not work solely through chemical action are regulated as medical devices. Their huge range of purposes, mechanisms of action and risks pose challenges for regulation. High-profile implantable device failures have fuelled concerns about the level of clinical evidence needed for market approval. Calls for more rigorous evaluation lack clarity about what kind of evaluation is appropriate, and are commonly interpreted as meaning more randomized controlled trials (RCTs). These are valuable where devices are genuinely new and claim to offer measurable therapeutic benefits. Where this is not the case, RCTs may be inappropriate and wasteful. METHODS: Starting with a set of ethical principles and basic precepts of clinical epidemiology, we developed a sequential decision-making algorithm for identifying when an RCT should be performed to evaluate new therapeutic devices, and when other methods, such as observational study designs and registry-based approaches, are acceptable. RESULTS: The algorithm clearly defines a group of devices where an RCT is deemed necessary, and the associated framework indicates that an IDEAL 2b study should be the default clinical evaluation method where it is not. CONCLUSIONS: The algorithm and recommendations are based on the principles of the IDEAL-D framework for medical device evaluation and appear eminently practicable. Their use would create a safer system for monitoring innovation, and facilitate more rapid detection of potential hazards to patients and the public.
Development and validation of early warning score systems for COVID-19 patients.
COVID-19 is a major, urgent, and ongoing threat to global health. Globally more than 24 million have been infected and the disease has claimed more than a million lives as of November 2020. Predicting which patients will need respiratory support is important to guiding individual patient treatment and also to ensuring sufficient resources are available. The ability of six common Early Warning Scores (EWS) to identify respiratory deterioration defined as the need for advanced respiratory support (high-flow nasal oxygen, continuous positive airways pressure, non-invasive ventilation, intubation) within a prediction window of 24 h is evaluated. It is shown that these scores perform sub-optimally at this specific task. Therefore, an alternative EWS based on the Gradient Boosting Trees (GBT) algorithm is developed that is able to predict deterioration within the next 24 h with high AUROC 94% and an accuracy, sensitivity, and specificity of 70%, 96%, 70%, respectively. The GBT model outperformed the best EWS (LDTEWS:NEWS), increasing the AUROC by 14%. Our GBT model makes the prediction based on the current and baseline measures of routinely available vital signs and blood tests.
Multivariate time-series analysis of biomarkers from a dengue cohort offers new approaches for diagnosis and prognosis.
Dengue is a major public health problem worldwide with distinct clinical manifestations: an acute presentation (dengue fever, DF) similar to other febrile illnesses (OFI) and a more severe, life-threatening form (severe dengue, SD). Due to nonspecific clinical presentation during the early phase of dengue infection, differentiating DF from OFI has remained a challenge, and current methods to determine severity of dengue remain poor early predictors. We present a prospective clinical cohort study conducted in Caracas, Venezuela from 2001-2005, designed to determine whether clinical and hematological parameters could distinguish DF from OFI, and identify early prognostic biomarkers of SD. From 204 enrolled suspected dengue patients, there were 111 confirmed dengue cases. Piecewise mixed effects regression and nonparametric statistics were used to analyze longitudinal records. Decreased serum albumin and fibrinogen along with increased D-dimer, thrombin-antithrombin complex, activated partial thromboplastin time and thrombin time were prognostic of SD on the day of defervescence. In the febrile phase, the day-to-day rates of change in serum albumin and fibrinogen concentration, along with platelet counts, were significantly decreased in dengue patients compared to OFI, while the day-to-day rates of change of lymphocytes (%) and thrombin time were increased. In dengue patients, the absolute lymphocytes to neutrophils ratio showed specific temporal increase, enabling classification of dengue patients entering the critical phase with an area under the ROC curve of 0.79. Secondary dengue patients had elongation of Thrombin time compared to primary cases while the D-dimer formation (fibrinolysis marker) remained always lower for secondary compared to primary cases. Based on partial analysis of 31 viral complete genomes, a high frequency of C-to-T transitions located at the third codon position was observed, suggesting deamination events with five major hot spots of amino acid polymorphic sites outside in non-structural proteins. No association of severe outcome was statistically significant for any of the five major polymorphic sites found. This study offers an improved understanding of dengue hemostasis and a novel way of approaching dengue diagnosis and disease prognosis using piecewise mixed effect regression modeling. It also suggests that a better discrimination of the day of disease can improve the diagnostic and prognostic classification power of clinical variables using ROC curve analysis. The piecewise mixed effect regression model corroborated key early clinical determinants of disease, and offers a time-series approach for future vaccine and pathogenesis clinical studies.
DECIDE-AI: a new reporting guideline and its relevance to artificial intelligence studies in radiology.
DECIDE-AI is a new, stage-specific reporting guideline for the early and live clinical evaluation of decision-support systems based on artificial intelligence (AI). It answers a need for more attention to the human factors influencing clinical AI performance and more transparent reporting of clinical studies investigating AI systems. Given the rapid expansion of AI systems and the concentration of related studies in radiology, these new standards are likely to find a place in radiological literature in the near future. This review highlights some of the specificities of AI as complex intervention, why a new reporting guideline was needed for early stage, live evaluation of this technology, and how DECIDE-AI and other AI reporting guidelines can be useful to radiologists and researchers.
Intraoperative Applications of Artificial Intelligence in Robotic Surgery: A Scoping Review of Current Development Stages and Levels of Autonomy.
OBJECTIVE: A scoping review of the literature was conducted to identify intraoperative artificial intelligence (AI) applications for robotic surgery under development and categorize them by (1) purpose of the applications, (2) level of autonomy, (3) stage of development, and (4) type of measured outcome. BACKGROUND: In robotic surgery, AI-based applications have the potential to disrupt a field so far based on a master-slave paradigm. However, there is no available overview about this technology's current stage of development and level of autonomy. METHODS: MEDLINE and EMBASE were searched between January 1, 2010 and May 21, 2022. Abstract screening, full-text review, and data extraction were performed independently by 2 reviewers. The level of autonomy was defined according to the Yang and colleagues' classification and stage of development according to the Idea, Development, Evaluation, Assessment, and Long-term follow-up framework. RESULTS: One hundred twenty-nine studies were included in the review. Ninety-seven studies (75%) described applications providing Robot Assistance (autonomy level 1), 30 studies (23%) application enabling Task Autonomy (autonomy level 2), and 2 studies (2%) application achieving Conditional autonomy (autonomy level 3). All studies were at Idea, Development, Evaluation, Assessment, and Long-term follow-up stage 0 and no clinical investigations on humans were found. One hundred sixteen (90%) conducted in silico or ex vivo experiments on inorganic material, 9 (7%) ex vivo experiments on organic material, and 4 (3%) performed in vivo experiments in porcine models. CONCLUSIONS: Clinical evaluation of intraoperative AI applications for robotic surgery is still in its infancy and most applications have a low level of autonomy. With increasing levels of autonomy, the evaluation focus seems to shift from AI-specific metrics to process outcomes, although common standards are needed to allow comparison between systems.
A case of disseminated autochthonous Cladophialophora bantiana infection in a renal transplant recipient in the UK.
Disease associated with Cladophialophora bantiana infection is uncommon but can be characterised by severe and life-threatening CNS involvement. Diagnosis is challenging due to both the infection's rarity and non-specific clinical presentation, which can mimic malignancy and infection caused by more common organisms. Transmission can occur via inhalation or inoculation through compromised skin, followed by haematogenous dissemination to the brain and other organs. We report a case of a 42-year-old renal transplant recipient with no travel history presenting with neurological symptoms and skin and lung lesions due to C bantiana infection. An aggressive treatment approach comprising combination antifungal therapy, surgical debridement, and withdrawal of immunosuppression resulted in disease control, although this treatment was complicated by voriconazole-induced skeletal fluorosis. This organism, more commonly encountered in tropical regions, has traditionally been considered imported into the UK by returning travellers, therefore this case of autochthonous infection could reflect an expanding range alongside global climactic shifts.
Cortico-thalamic tremor circuits and their associations with deep brain stimulation effects in essential tremor.
Essential tremor (ET) is one of the most common movement disorders in adults. Deep brain stimulation (DBS) of the ventralis intermediate nucleus (VIM) of the thalamus and/or the posterior subthalamic area (PSA) has been shown to provide significant tremor suppression in patients with ET, but with significant inter-patient variability and habituation to the stimulation. Several non-invasive neuromodulation techniques targeting other parts of the central nervous system, including cerebellar, motor cortex, or peripheral nerves, have also been developed for treating ET, but the clinical outcomes remain inconsistent. Existing studies suggest that pathology in ET may emerge from multiple cortical and subcortical areas, but its exact mechanisms remain unclear. By simultaneously capturing neural activities from motor cortices and thalami, and hand tremor signals recorded via accelerometers in fifteen human subjects who have undergone lead implantations for DBS, we systematically characterized the efferent and afferent cortico-thalamic tremor networks. Through the comparisons of these network characteristics and tremor amplitude between DBS OFF and ON conditions, we further investigated the associations between different tremor network characteristics and the magnitude of DBS effect. Our findings implicate the thalamus, specifically the contralateral hemisphere, as the primary generator of tremor in ET, with a significant contribution of the ipsilateral hemisphere as well. Although there is no direct correlation between the cortico-tremor connectivity and tremor power or reduced tremor by DBS, the strength of connectivity from the motor cortex to the thalamus and vice versa at tremor frequency predicts baseline tremor power and effect of DBS. Interestingly, there is no correlation between these two connectivity pathways themselves, suggesting that, independent of the subcortical pathway, the motor cortex appears to play a relatively distinct role, possibly mediated through an afferent/feedback loop in the propagation of tremor. DBS has a greater clinical effect in those with stronger cortico-thalamo-tremor connectivity involving the contralateral thalamus, which is also associated with bigger and more stable tremor measured with an accelerometer. Interestingly, stronger cross-hemisphere coupling between left and right thalami is associated with more unstable tremor. Together this study provides important insights into a better understanding of the cortico-thalamic tremor generating network and its implication for the development of patient-specific therapeutic approaches for ET.
HDE-Array: Development and Validation of a New Dry Electrode Array Design to Acquire HD-sEMG for Hand Position Estimation.
This paper aims to introduce HDE-Array (High-Density Electrode Array), a novel dry electrode array for acquiring High-Density surface electromyography (HD-sEMG) for hand position estimation through RPC-Net (Recursive Prosthetic Control Network), a neural network defined in a previous study. We aim to demonstrate the hypothesis that the position estimates returned by RPC-Net using HD-sEMG signals acquired with HDE-Array are as accurate as those obtained from signals acquired with gel electrodes. We compared the results, in terms of precision of hand position estimation by RPC-Net, using signals acquired by traditional gel electrodes and by HDE-Array. As additional validation, we performed a variance analysis to confirm that the presence of only two rows of electrodes does not result in an excessive loss of information, and we characterized the electrode-skin impedance to assess the effects of the voltage divider effect and power line interference. Performance tests indicated that RPC-Net, used with HDE-Array, achieved comparable or superior results to those observed when used with the gel electrode setup. The dry electrodes demonstrated effective performance even with a simplified setup, highlighting potential cost and usability benefits. These results suggest improvements in the accessibility and user-friendliness of upper-limb rehabilitation devices and underscore the potential of HDE-Array and RPC-Net to revolutionize control for medical and non-medical applications.
Predicting future fallers in Parkinson's disease using kinematic data over a period of 5 years.
Parkinson's disease (PD) increases fall risk, leading to injuries and reduced quality of life. Accurate fall risk assessment is crucial for effective care planning. Traditional assessments are subjective and time-consuming, while recent assessment methods based on wearable sensors have been limited to 1-year follow-ups. This study investigated whether a short sensor-based assessment could predict falls over up to 5 years. Data from 104 people with PD without prior falls were collected using six wearable sensors during a 2-min walk and a 30-s postural sway task. Five machine learning classifiers analysed the data. The Random Forest classifier performed best, achieving 78% accuracy (AUC = 0.85) at 60 months. Most models showed excellent performance at 24 months (AUC > 0.90, accuracy 84-92%). Walking and postural variability measures were key predictors. Adding clinicodemographic data, particularly age, improved model performance. Wearable sensors combined with machine learning can effectively predict fall risk, enhancing PD management and prevention strategies.
The Use of Fluorescent Markers to Detect and Delineate Head and Neck Cancer: A Scoping Review
ABSTRACTObjectivesThe aim of surgery for head and neck squamous cell carcinoma (HNSCC) is to achieve clear resection margins, whilst preserving function and cosmesis. Fluorescent markers have demonstrated potential in the intraoperative visualisation and delineation of tumours, such as glioma, with consequent improvements in resection. The purpose of this scoping review was to identify and compare the fluorescent markers that have been used to detect and delineate HNSCC to date.MethodsA literature search was performed using the Ovid MEDLINE, Ovid Embase, Cochrane CENTRAL, ClinicalTrials.gov and ICTRP databases. Primary human studies published through September 2023 demonstrating the use of fluorescent markers to visualise HNSCC were selected and reviewed independently by two authors.ResultsThe search strategy identified 5776 records. Two hundred and forty‐four full texts were reviewed, and sixty‐five eligible reports were included. The most used fluorescent markers in the included studies were indocyanine green (ICG) (n = 14), toluidine blue (n = 11), antibodies labelled with IRDye800CW (n = 10) and 5‐aminolevulinic acid (5‐ALA) (n = 8). Toluidine blue and ICG both have limited specificity, although novel targeted options derived from ICG may be more effective. 5‐ALA has been demonstrated as a topical marker and, recently, via enteral administration but it is associated with photosensitivity reactions. The fluorescently labelled antibodies cetuximab‐IRDye800CW and panitumumab‐IRDye800CW are promising options being investigated by ongoing trials.ConclusionMultiple safe fluorescent markers have emerged which may aid the surgical resection of HNSCC. Further research in larger cohorts is required to identify which marker should be considered gold standard.
Department Wide Validation in Digital Pathology-Experience from an Academic Teaching Hospital Using the UK Royal College of Pathologists' Guidance.
AIM: we describe our experience of validating departmental pathologists for digital pathology reporting, based on the UK Royal College of Pathologists (RCPath) "Best Practice Recommendations for Implementing Digital Pathology (DP)," at a large academic teaching hospital that scans 100% of its surgical workload. We focus on Stage 2 of validation (prospective experience) prior to full validation sign-off. METHODS AND RESULTS: twenty histopathologists completed Stage 1 of the validation process and subsequently completed Stage 2 validation, prospectively reporting a total of 3777 cases covering eight specialities. All cases were initially viewed on digital whole slide images (WSI) with relevant parameters checked on glass slides, and discordances were reconciled before the case was signed out. Pathologists kept an electronic log of the cases, the preferred reporting modality used, and their experiences. At the end of each validation, a summary was compiled and reviewed with a mentor. This was submitted to the DP Steering Group who assessed the scope of cases and experience before sign-off for full validation. A total of 1.3% (49/3777) of the cases had a discordance between WSI and glass slides. A total of 61% (30/49) of the discordances were categorised as a minor error in a supplementary parameter without clinical impact. The most common reasons for diagnostic discordances across specialities included identification and grading of dysplasia, assessment of tumour invasion, identification of small prognostic or diagnostic objects, interpretation of immunohistochemistry/special stains, and mitotic count assessment. Pathologists showed similar mean diagnostic confidences (on Likert scale from 0 to 7) with a mean of 6.8 on digital and 6.9 on glass slide reporting. CONCLUSION: we describe one of the first real-world experiences of a department-wide effort to implement, validate, and roll out digital pathology reporting by applying the RCPath Recommendations for Implementing DP. We have shown a very low rate of discordance between WSI and glass slides.