Blood Harmonisation of Endoscopic Transsphenoidal Surgical Video Frames on Phantom Models.

Physical phantom models have been integral to surgical training, yet they lack realism and are unable to replicate the presence of blood resulting from surgical actions. Existing domain transfer methods aim to enhance realism, but none facilitate blood simulation. This study investigates the overlay of blood on images acquired during endoscopic transsphenoidal pituitary surgery on phantom models. The process involves employing manual techniques using the GIMP image manipulation application and automated methods using pythons Blend Modes module. We then approach this as an image harmonisation task to assess its practicality and feasibility. Our evaluation uses Structural Similarity Index Measure and Laplacian metrics. The results we obtained emphasize the significance of image harmonisation, offering substantial insights within the surgical field. Our work is a step towards investigating data-driven models that can simulate blood for increased realism during surgical training on phantom models.

Reduction of surgical complications via 3D models during robotic assisted radical prostatectomy: review of current evidence and meta-analysis.

The use of 3-dimensional (3D) technology has become increasingly popular across different surgical specialities to improve surgical outcomes. 3D technology has the potential to be applied to robotic assisted radical prostatectomy to visualise the patient's prostate anatomy to be used as a preoperative and peri operative surgical guide. This literature review aims to analyse all relevant pre-existing research on this topic. Following PRISMA guidelines, a search was carried out on PubMed, Medline, and Scopus. A total of seven studies were included in this literature review; two of which used printed-3D models and the remaining five using virtual augmented reality (AR) 3D models. Results displayed variation with select studies presenting that the use of 3D models enhances surgical outcomes and reduces complications whilst others displayed conflicting evidence. The use of 3D modelling within surgery has potential to improve various areas. This includes the potential surgical outcomes, including complication rates, due to improved planning and education.

Autonomous navigation of catheters and guidewires in mechanical thrombectomy using inverse reinforcement learning.

PURPOSE: Autonomous navigation of catheters and guidewires can enhance endovascular surgery safety and efficacy, reducing procedure times and operator radiation exposure. Integrating tele-operated robotics could widen access to time-sensitive emergency procedures like mechanical thrombectomy (MT). Reinforcement learning (RL) shows potential in endovascular navigation, yet its application encounters challenges without a reward signal. This study explores the viability of autonomous guidewire navigation in MT vasculature using inverse reinforcement learning (IRL) to leverage expert demonstrations. METHODS: Employing the Simulation Open Framework Architecture (SOFA), this study established a simulation-based training and evaluation environment for MT navigation. We used IRL to infer reward functions from expert behaviour when navigating a guidewire and catheter. We utilized the soft actor-critic algorithm to train models with various reward functions and compared their performance in silico. RESULTS: We demonstrated feasibility of navigation using IRL. When evaluating single- versus dual-device (i.e. guidewire versus catheter and guidewire) tracking, both methods achieved high success rates of 95% and 96%, respectively. Dual tracking, however, utilized both devices mimicking an expert. A success rate of 100% and procedure time of 22.6 s were obtained when training with a reward function obtained through 'reward shaping'. This outperformed a dense reward function (96%, 24.9 s) and an IRL-derived reward function (48%, 59.2 s). CONCLUSIONS: We have contributed to the advancement of autonomous endovascular intervention navigation, particularly MT, by effectively employing IRL based on demonstrator expertise. The results underscore the potential of using reward shaping to efficiently train models, offering a promising avenue for enhancing the accessibility and precision of MT procedures. We envisage that future research can extend our methodology to diverse anatomical structures to enhance generalizability.

Artificial intelligence in the autonomous navigation of endovascular interventions: a systematic review.

Background: Autonomous navigation of catheters and guidewires in endovascular interventional surgery can decrease operation times, improve decision-making during surgery, and reduce operator radiation exposure while increasing access to treatment. Objective: To determine from recent literature, through a systematic review, the impact, challenges, and opportunities artificial intelligence (AI) has for the autonomous navigation of catheters and guidewires for endovascular interventions. Methods: PubMed and IEEEXplore databases were searched to identify reports of AI applied to autonomous navigation methods in endovascular interventional surgery. Eligibility criteria included studies investigating the use of AI in enabling the autonomous navigation of catheters/guidewires in endovascular interventions. Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), articles were assessed using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). PROSPERO: CRD42023392259. Results: Four hundred and sixty-two studies fulfilled the search criteria, of which 14 studies were included for analysis. Reinforcement learning (RL) (9/14, 64%) and learning from expert demonstration (7/14, 50%) were used as data-driven models for autonomous navigation. These studies evaluated models on physical phantoms (10/14, 71%) and in-silico (4/14, 29%) models. Experiments within or around the blood vessels of the heart were reported by the majority of studies (10/14, 71%), while non-anatomical vessel platforms "idealized" for simple navigation were used in three studies (3/14, 21%), and the porcine liver venous system in one study. We observed that risk of bias and poor generalizability were present across studies. No procedures were performed on patients in any of the studies reviewed. Moreover, all studies were limited due to the lack of patient selection criteria, reference standards, and reproducibility, which resulted in a low level of evidence for clinical translation. Conclusion: Despite the potential benefits of AI applied to autonomous navigation of endovascular interventions, the field is in an experimental proof-of-concept stage, with a technology readiness level of 3. We highlight that reference standards with well-identified performance metrics are crucial to allow for comparisons of data-driven algorithms proposed in the years to come. Systematic review registration: identifier: CRD42023392259.

Assessing informative tract segmentation and nTMS for pre-operative planning.

BACKGROUND: Deep learning-based (DL) methods are the best-performing methods for white matter tract segmentation in anatomically healthy subjects. However, tract annotations are variable or absent in clinical data and manual annotations are especially difficult in patients with tumors where normal anatomy may be distorted. Direct cortical and subcortical stimulation is the gold standard ground truth to determine the cortical and sub-cortical lo- cation of motor-eloquent areas intra-operatively. Nonetheless, this technique is invasive, prolongs the surgical procedure, and may cause patient fatigue. Navigated Transcranial Magnetic Stimulation (nTMS) has a well-established correlation to direct cortical stimulation for motor mapping and the added advantage of being able to be acquired pre-operatively. NEW METHOD: In this work, we evaluate the feasibility of using nTMS motor responses as a method to assess corticospinal tract (CST) binary masks and estimated uncertainty generated by a DL-based tract segmentation in patients with diffuse gliomas. RESULTS: Our results show CST binary masks have a high overlap coefficient (OC) with nTMS response masks. A strong negative correlation is found between estimated uncertainty and nTMS response mask distance to the CST binary mask. COMPARISON WITH EXISTING METHODS: We compare our approach (UncSeg) with the state-of-the-art TractSeg in terms of OC between the CST binary masks and nTMS response masks. CONCLUSIONS: In this study, we demonstrate that estimated uncertainty from UncSeg is a good measure of the agreement between the CST binary masks and nTMS response masks distance to the CST binary mask boundary.

Thalamostriatal disconnection underpins long-term seizure freedom in frontal lobe epilepsy surgery.

Around 50% of patients undergoing frontal lobe surgery for focal drug-resistant epilepsy become seizure free post-operatively; however, only about 30% of patients remain seizure free in the long-term. Early seizure recurrence is likely to be caused by partial resection of the epileptogenic lesion, whilst delayed seizure recurrence can occur even if the epileptogenic lesion has been completely excised. This suggests a coexistent epileptogenic network facilitating ictogenesis in close or distant dormant epileptic foci. As thalamic and striatal dysregulation can support epileptogenesis and disconnection of cortico-thalamostriatal pathways through hemispherotomy or neuromodulation can improve seizure outcome regardless of focality, we hypothesize that projections from the striatum and the thalamus to the cortex may contribute to this common epileptogenic network. To this end, we retrospectively reviewed a series of 47 consecutive individuals who underwent surgery for drug-resistant frontal lobe epilepsy. We performed voxel-based and tractography disconnectome analyses to investigate shared patterns of disconnection associated with long-term seizure freedom. Seizure freedom after 3 and 5 years was independently associated with disconnection of the anterior thalamic radiation and anterior cortico-striatal projections. This was also confirmed in a subgroup of 29 patients with complete resections, suggesting these pathways may play a critical role in supporting the development of novel epileptic networks. Our study indicates that network dysfunction in frontal lobe epilepsy may extend beyond the resection and putative epileptogenic zone. This may be critical in the pathogenesis of delayed seizure recurrence as thalamic and striatal networks may promote epileptogenesis and disconnection may underpin long-term seizure freedom.

Seudoaneurisma de la arteria femoral lateral: revisión sistemática a propósito de una complicación infrecuente en pacientes con fracturas pertrocantéricas / Lateral femoral artery pseudoaneurisms: A case report and systematic review of a rare complication of pertrochanteric fractures

Introducción: Se presenta un caso clínico de seudoaneurisma de la arteria femoral circunfleja lateral secundario a una fractura pertrocantérica de cadera. Materiales y métodos: Como el cuadro y su localización son infrecuentes, se llevó a cabo una revisión bibliográfica sistematizada que incluyó todos los casos publicados sobre esta enfermedad (n = 40) en los últimos 15 años. Resultados: No se hallaron asociaciones estadísticamente significativas entre ninguna de las variables estudiadas. Sin embargo, parece existir cierto consenso en mantener una alta sospecha clínica para una intervención precoz y así obtener mejores resultados. Tanto su etiología como su localización se relacionan con la morfología de la fractura, el gesto quirúrgico y el material de osteosíntesis. Asimismo, hay una tendencia mayor a utilizar la angiotomografía para el diagnóstico y la localización del seudoaneurisma. Conclusiones: Nuestra paciente es el primer caso de resolución espontánea. Es fundamental conocer esta complicación tan poco frecuente para optimizar los resultados terapéuticos. Esta revisión, la más reciente sobre el tema, es muy útil para enumerar y subrayar los aspectos más importantes sobre el manejo y la prevención de los seudoaneurismas secundarios a una fractura de cadera. Nivel de Evidencia: IV

Introduction: We present a case of a lateral circumflex femoral artery pseudoaneurysm associated with pertrochanteric hip fracture. Materials and methods: We performed a systematic review considering all cases published in the last 15 years about this pathology (n=40). Results: No statistically significant associations were found between any of the variables studied. However, there seems to be some consensus in maintaining a high clinical suspicion for early intervention, thus obtaining better outcomes. Both its etiology and location are related to the morphology of the fracture, the surgical procedure, and the osteosynthesis material. Likewise, there is a greater tendency to use CT angiography for the diagnosis and localization of the pseudoaneurysm. Conclusion: Our patient is the first reported case of spontaneous resolution. Knowing this rare complication is essential to optimize therapeutic results. This review, the most recent on the subject, is very useful in listing and highlighting the most important aspects of the management and prevention of pseudoaneurysms secondary to hip fracture. Level of Evidence: IV

Comparison of robotic and manual implantation of intracerebral electrodes: a single-centre, single-blinded, randomised controlled trial.

There has been a significant rise in robotic trajectory guidance devices that have been utilised for stereotactic neurosurgical procedures. These devices have significant costs and associated learning curves. Previous studies reporting devices usage have not undertaken prospective parallel-group comparisons before their introduction, so the comparative differences are unknown. We study the difference in stereoelectroencephalography electrode implantation time between a robotic trajectory guidance device (iSYS1) and manual frameless implantation (PAD) in patients with drug-refractory focal epilepsy through a single-blinded randomised control parallel-group investigation of SEEG electrode implantation, concordant with CONSORT statement. Thirty-two patients (18 male) completed the trial. The iSYS1 returned significantly shorter median operative time for intracranial bolt insertion, 6.36 min (95% CI 5.72-7.07) versus 9.06 min (95% CI 8.16-10.06), p = 0.0001. The PAD group had a better median target point accuracy 1.58 mm (95% CI 1.38-1.82) versus 1.16 mm (95% CI 1.01-1.33), p = 0.004. The mean electrode implantation angle error was 2.13° for the iSYS1 group and 1.71° for the PAD groups (p = 0.023). There was no statistically significant difference for any other outcome. Health policy and hospital commissioners should consider these differences in the context of the opportunity cost of introducing robotic devices.Trial registration: ISRCTN17209025 ( https://doi.org/10.1186/ISRCTN17209025 ).

Patient-specific prediction of SEEG electrode bending for stereotactic neurosurgical planning.

PURPOSE : Electrode bending observed after stereotactic interventions is typically not accounted for in either computer-assisted planning algorithms, where straight trajectories are assumed, or in quality assessment, where only metrics related to entry and target points are reported. Our aim is to provide a fully automated and validated pipeline for the prediction of stereo-electroencephalography (SEEG) electrode bending. METHODS : We transform electrodes of 86 cases into a common space and compare features-based and image-based neural networks on their ability to regress local displacement ([Formula: see text]) or electrode bending ([Formula: see text]). Electrodes were stratified into six groups based on brain structures at the entry and target point. Models, both with and without Monte Carlo (MC) dropout, were trained and validated using tenfold cross-validation. RESULTS : mage-based models outperformed features-based models for all groups, and models that predicted [Formula: see text] performed better than for [Formula: see text]. Image-based model prediction with MC dropout resulted in lower mean squared error (MSE) with improvements up to 12.9% ([Formula: see text]) and 39.9% ([Formula: see text]), compared to no dropout. Using an image of brain tissue types (cortex, white and deep grey matter) resulted in similar, and sometimes better performance, compared to using a T1-weighted MRI when predicting [Formula: see text]. When inferring trajectories of image-based models (brain tissue types), 86.9% of trajectories had an MSE[Formula: see text] mm. CONCLUSION : An image-based approach regressing local displacement with an image of brain tissue types resulted in more accurate electrode bending predictions compared to other approaches, inputs, and outputs. Future work will investigate the integration of electrode bending into planning and quality assessment algorithms.

A generative model of hyperelastic strain energy density functions for multiple tissue brain deformation.

PURPOSE: Estimation of brain deformation is crucial during neurosurgery. Whilst mechanical characterisation captures stress-strain relationships of tissue, biomechanical models are limited by experimental conditions. This results in variability reported in the literature. The aim of this work was to demonstrate a generative model of strain energy density functions can estimate the elastic properties of tissue using observed brain deformation. METHODS: For the generative model a Gaussian Process regression learns elastic potentials from 73 manuscripts. We evaluate the use of neo-Hookean, Mooney-Rivlin and 1-term Ogden meta-models to guarantee stability. Single and multiple tissue experiments validate the ability of our generative model to estimate tissue properties on a synthetic brain model and in eight temporal lobe resection cases where deformation is observed between pre- and post-operative images. RESULTS: Estimated parameters on a synthetic model are close to the known reference with a root-mean-square error (RMSE) of 0.1 mm and 0.2 mm between surface nodes for single and multiple tissue experiments. In clinical cases, we were able to recover brain deformation from pre- to post-operative images reducing RMSE of differences from 1.37 to 1.08 mm on the ventricle surface and from 5.89 to 4.84 mm on the resection cavity surface. CONCLUSION: Our generative model can capture uncertainties related to mechanical characterisation of tissue. When fitting samples from elastography and linear studies, all meta-models performed similarly. The Ogden meta-model performed the best on hyperelastic studies. We were able to predict elastic parameters in a reference model on a synthetic phantom. However, deformation observed in clinical cases is only partly explained using our generative model.

Refining Planning for Stereoelectroencephalography: A Prospective Validation of Spatial Priors for Computer-Assisted Planning With Application of Dynamic Learning.

Objective: Stereoelectroencephalography (SEEG) is a procedure in which many electrodes are stereotactically implanted within different regions of the brain to estimate the epileptogenic zone in patients with drug-refractory focal epilepsy. Computer-assisted planning (CAP) improves risk scores, gray matter sampling, orthogonal drilling angles to the skull and intracerebral length in a fraction of the time required for manual planning. Due to differences in planning practices, such algorithms may not be generalizable between institutions. We provide a prospective validation of clinically feasible trajectories using "spatial priors" derived from previous implantations and implement a machine learning classifier to adapt to evolving planning practices. Methods: Thirty-two patients underwent consecutive SEEG implantations utilizing computer-assisted planning over 2 years. Implanted electrodes from the first 12 patients (108 electrodes) were used as a training set from which entry and target point spatial priors were generated. CAP was then prospectively performed using the spatial priors in a further test set of 20 patients (210 electrodes). A K-nearest neighbor (K-NN) machine learning classifier was implemented as an adaptive learning method to modify the spatial priors dynamically. Results: All of the 318 prospective computer-assisted planned electrodes were implanted without complication. Spatial priors developed from the training set generated clinically feasible trajectories in 79% of the test set. The remaining 21% required entry or target points outside of the spatial priors. The K-NN classifier was able to dynamically model real-time changes in the spatial priors in order to adapt to the evolving planning requirements. Conclusions: We provide spatial priors for common SEEG trajectories that prospectively integrate clinically feasible trajectory planning practices from previous SEEG implantations. This allows institutional SEEG experience to be incorporated and used to guide future implantations. The deployment of a K-NN classifier may improve the generalisability of the algorithm by dynamically modifying the spatial priors in real-time as further implantations are performed.

Taking the Pressure Off the Patient - Understanding Digital Rectal Examinations on a Real Subject.

Better understanding of palpation techniques during unsighted physical examinations has mostly been limited to qualitative and quantitative studies of performance of experts whilst conducting examinations on plastic benchtop models. However, little is known about their performance when conducting such examinations on real subjects. OBJECTIVE: The aim of this paper is to better understand palpation techniques of experts whilst conducting a Digital Rectal Examination on a real subject. METHODS: We recruited four consultants from relevant specialties and asked them to conduct two DREs on a Rectal Teaching Assistant whilst wearing small position and pressure sensors on their examining finger. We segmented the relevant anatomy from an MRI taken of the pelvic region, registered 3D models and analysed retrospectively performance in relation to executed tasks, supination/pronation, palpation convex hull and pressure applied. RESULTS: Primary care consultants examined the anatomy more holistically compared to secondary care experts, the maximum pressure applied across experiments is 3.3N, overall the pressure applied on the prostate is higher than that applied to rectal walls, and the urologist participant not only applied the highest pressure but also did so with the highest most prominent frequency (15.4 and 25.3 Hz). CONCLUSIONS: The results of our research allow for better understanding of experts' technical performance from relevant specialities when conducting a DRE, and suggest the range of pressure applied whilst palpating anatomy. SIGNIFICANCE: This research will be valuable in improving the design of haptics-based learning tools, as well as in encouraging reflection on palpation styles across different specialities to develop metrics of performance.

Optimizing Trajectories for Cranial Laser Interstitial Thermal Therapy Using Computer-Assisted Planning: A Machine Learning Approach.

Laser interstitial thermal therapy (LITT) is an alternative to open surgery for drug-resistant focal mesial temporal lobe epilepsy (MTLE). Studies suggest maximal ablation of the mesial hippocampal head and amygdalohippocampal complex (AHC) improves seizure freedom rates while better neuropsychological outcomes are associated with sparing of the parahippocampal gyrus (PHG). Optimal trajectories avoid sulci and CSF cavities and maximize distance from vasculature. Computer-assisted planning (CAP) improves these metrics, but the combination of entry and target zones has yet to be determined to maximize ablation of the AHC while sparing the PHG. We apply a machine learning approach to predict entry and target parameters and utilize these for CAP. Ten patients with hippocampal sclerosis were identified from a prospectively managed database. CAP LITT trajectories were generated using entry regions that include the inferior occipital, middle occipital, inferior temporal, and middle temporal gyri. Target points were varied by sequential AHC erosions and transformations of the centroid of the amygdala. A total of 7600 trajectories were generated, and ablation volumes of the AHC and PHG were calculated. Two machine learning approaches (random forest and linear regression) were investigated to predict composite ablation scores and determine entry and target point combinations that maximize ablation of the AHC while sparing the PHG. Random forest and linear regression predictions had a high correlation with the calculated values in the test set (ρ = 0.7) for both methods. Maximal composite ablation scores were associated with entry points around the junction of the inferior occipital, middle occipital, and middle temporal gyri. The optimal target point was the anteromesial amygdala. These parameters were then used with CAP to generate clinically feasible trajectories that optimize safety metrics. Machine learning techniques accurately predict composite ablation score. Prospective studies are required to determine if this improves seizure-free outcome while reducing neuropsychological morbidity following LITT for MTLE.

Automatic segmentation of stereoelectroencephalography (SEEG) electrodes post-implantation considering bending.

PURPOSE: The accurate and automatic localisation of SEEG electrodes is crucial for determining the location of epileptic seizure onset. We propose an algorithm for the automatic segmentation of electrode bolts and contacts that accounts for electrode bending in relation to regional brain anatomy. METHODS: Co-registered post-implantation CT, pre-implantation MRI, and brain parcellation images are used to create regions of interest to automatically segment bolts and contacts. Contact search strategy is based on the direction of the bolt with distance and angle constraints, in addition to post-processing steps that assign remaining contacts and predict contact position. We measured the accuracy of contact position, bolt angle, and anatomical region at the tip of the electrode in 23 post-SEEG cases comprising two different surgical approaches when placing a guiding stylet close to and far from target point. Local and global bending are computed when modelling electrodes as elastic rods. RESULTS: Our approach executed on average in 36.17 s with a sensitivity of 98.81% and a positive predictive value (PPV) of 95.01%. Compared to manual segmentation, the position of contacts had a mean absolute error of 0.38 mm and the mean bolt angle difference of [Formula: see text] resulted in a mean displacement error of 0.68 mm at the tip of the electrode. Anatomical regions at the tip of the electrode were in strong concordance with those selected manually by neurosurgeons, [Formula: see text], with average distance between regions of 0.82 mm when in disagreement. Our approach performed equally in two surgical approaches regardless of the amount of electrode bending. CONCLUSION: We present a method robust to electrode bending that can accurately segment contact positions and bolt orientation. The techniques presented in this paper will allow further characterisation of bending within different brain regions.

Distributed and dynamic intracellular organization of extracellular information.

Although cells respond specifically to environments, how environmental identity is encoded intracellularly is not understood. Here, we study this organization of information in budding yeast by estimating the mutual information between environmental transitions and the dynamics of nuclear translocation for 10 transcription factors. Our method of estimation is general, scalable, and based on decoding from single cells. The dynamics of the transcription factors are necessary to encode the highest amounts of extracellular information, and we show that information is transduced through two channels: Generalists (Msn2/4, Tod6 and Dot6, Maf1, and Sfp1) can encode the nature of multiple stresses, but only if stress is high; specialists (Hog1, Yap1, and Mig1/2) encode one particular stress, but do so more quickly and for a wider range of magnitudes. In particular, Dot6 encodes almost as much information as Msn2, the master regulator of the environmental stress response. Each transcription factor reports differently, and it is only their collective behavior that distinguishes between multiple environmental states. Changes in the dynamics of the localization of transcription factors thus constitute a precise, distributed internal representation of extracellular change. We predict that such multidimensional representations are common in cellular decision-making.

Real-time visualisation and analysis of internal examinations--seeing the unseen.

Internal examinations such as Digital Rectal Examination (DRE) and bimanual Vaginal Examination (BVE) are routinely performed for early diagnosis of cancer and other diseases. Although they are recognised as core skills to be taught on a medical curriculum, they are difficult to learn and teach due to their unsighted nature. We present a framework that combines a visualisation and analysis tool with position and pressure sensors to enable the study of internal examinations and provision of real-time feedback. This approach is novel as it allows for real-time continuous trajectory and pressure data to be obtained for the complete examination, which may be used for teaching and assessment. Experiments were conducted performing DRE and BVE on benchtop models, and BVE on Gynaecological Teaching Assistants (GTA). The results obtained suggest that the proposed methodology may provide an insight into what constitutes an adequate DRE or BVE, provide real-time feedback tools for learning and assessment, and inform haptics-based simulator design.