54 research outputs found

    High fidelity simulation of the endoscopic transsphenoidal approach: Validation of the UpSurgeOn TNS Box

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    Objective: Endoscopic endonasal transsphenoidal surgery is an established technique for the resection of sellar and suprasellar lesions. The approach is technically challenging and has a steep learning curve. Simulation is a growing training tool, allowing the acquisition of technical skills pre-clinically and potentially resulting in a shorter clinical learning curve. We sought validation of the UpSurgeOn Transsphenoidal (TNS) Box for the endoscopic endonasal transsphenoidal approach to the pituitary fossa./ Methods: Novice, intermediate and expert neurosurgeons were recruited from multiple centres. Participants were asked to perform a sphenoidotomy using the TNS model. Face and content validity were evaluated using a post-task questionnaire. Construct validity was assessed through post-hoc blinded scoring of operative videos using a Modified Objective Structured Assessment of Technical Skills (mOSAT) and a Task-Specific Technical Skill scoring system./ Results: Fifteen participants were recruited of which n = 10 (66.6%) were novices and n = 5 (33.3%) were intermediate and expert neurosurgeons. Three intermediate and experts (60%) agreed that the model was realistic. All intermediate and experts (n = 5) strongly agreed or agreed that the TNS model was useful for teaching the endonasal transsphenoidal approach to the pituitary fossa. The consensus-derived mOSAT score was 16/30 (IQR 14–16.75) for novices and 29/30 (IQR 27–29) for intermediate and experts (p < 0.001, Mann–Whitney U). The median Task-Specific Technical Skill score was 10/20 (IQR 8.25–13) for novices and 18/20 (IQR 17.75–19) for intermediate and experts (p < 0.001, Mann-Whitney U). Interrater reliability was 0.949 (CI 0.983–0.853) for OSATS and 0.945 (CI 0.981–0.842) for Task-Specific Technical Skills. Suggested improvements for the model included the addition of neuro-vascular anatomy and arachnoid mater to simulate bleeding vessels and CSF leak, respectively, as well as improvement in materials to reproduce the consistency closer to that of human tissue and bone./ Conclusion: The TNS Box simulation model has demonstrated face, content, and construct validity as a simulator for the endoscopic endonasal transsphenoidal approach. With the steep learning curve associated with endoscopic approaches, this simulation model has the potential as a valuable training tool in neurosurgery with further improvements including advancing simulation materials, dynamic models (e.g., with blood flow) and synergy with complementary technologies (e.g., artificial intelligence and augmented reality)

    Current and Future Advances in Surgical Therapy for Pituitary Adenoma

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    The vital physiological role of the pituitary gland, alongside its proximal critical neurovascular structures means pituitary adenomas cause significant morbidity or mortality. Whilst enormous advancements have been made in the surgical care of pituitary adenomas, treatment failure and recurrence remain challenges. To meet these clinical challenges, there has been an enormous expansion of novel medical technologies (e.g. endoscopy, advanced imaging, artificial intelligence). These innovations have the potential to benefit each step of the patient journey, and ultimately, drive improved outcomes. Earlier and more accurate diagnosis addresses this in part. Analysis of novel patient data sets, such as automated facial analysis or natural language processing of medical records holds potential in achieving an earlier diagnosis. After diagnosis, treatment decision-making and planning will benefit from radiomics and multimodal machine learning models. Surgical safety and effectiveness will be transformed by smart simulation methods for trainees. Next-generation imaging techniques and augmented reality will enhance surgical planning and intraoperative navigation. Similarly, the future armamentarium of pituitary surgeons, including advanced optical devices, smart instruments and surgical robotics, will augment the surgeon's abilities. Intraoperative support to team members will benefit from a surgical data science approach, utilising machine learning analysis of operative videos to improve patient safety and orientate team members to a common workflow. Postoperatively, early detection of individuals at risk of complications and prediction of treatment failure through neural networks of multimodal datasets will support earlier intervention, safer hospital discharge, guide follow-up and adjuvant treatment decisions. Whilst advancements in pituitary surgery hold promise to enhance the quality of care, clinicians must be the gatekeepers of technological translation, ensuring systematic assessment of risk and benefit. In doing so, the synergy between these innovations can be leveraged to drive improved outcomes for patients of the future

    Image-guidance in endoscopic pituitary surgery: an in-silico study of errors involved in tracker-based techniques

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    Background: Endoscopic endonasal surgery is an established minimally invasive technique for resecting pituitary adenomas. However, understanding orientation and identifying critical neurovascular structures in this anatomically dense region can be challenging. In clinical practice, commercial navigation systems use a tracked pointer for guidance. Augmented Reality (AR) is an emerging technology used for surgical guidance. It can be tracker based or vision based, but neither is widely used in pituitary surgery. Methods: This pre-clinical study aims to assess the accuracy of tracker-based navigation systems, including those that allow for AR. Two setups were used to conduct simulations: (1) the standard pointer setup, tracked by an infrared camera; and (2) the endoscope setup that allows for AR, using reflective markers on the end of the endoscope, tracked by infrared cameras. The error sources were estimated by calculating the Euclidean distance between a point’s true location and the point’s location after passing it through the noisy system. A phantom study was then conducted to verify the in-silico simulation results and show a working example of image-based navigation errors in current methodologies. Results: The errors of the tracked pointer and tracked endoscope simulations were 1.7 and 2.5 mm respectively. The phantom study showed errors of 2.14 and 3.21 mm for the tracked pointer and tracked endoscope setups respectively. Discussion: In pituitary surgery, precise neighboring structure identification is crucial for success. However, our simulations reveal that the errors of tracked approaches were too large to meet the fine error margins required for pituitary surgery. In order to achieve the required accuracy, we would need much more accurate tracking, better calibration and improved registration techniques

    Comparison of the lateral supraorbital approach and endoscopic endonasal transclival approach to basilar apex aneurysms among other possible applications of the endoscopic endonasal technique to vascular neurosurgery: anatomic and clinical study.

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    Abstract Introduction. The expansion of the endoscopic endonasal approach in neurosurgery during the last three decades recently led the neurosurgical clinical interest to the investigation of further application of this technique, namely to neurovascular pathologies. Cadaver dissections studies have represented the milestone in the progressive application of this technique. Integrating anatomical studies with advanced visualization tools and quantification methods increases their impact toward clinical application. Material and methods. The main endoscopic endonasal approaches were performed and exposure of the vascular intracranial structures was analyzed: the anterior communicating artery complex was investigated through the transplanum transtuberculum approach; the transsphenoidal approach to the sellar area was performed for the exposure of the intracavernous internal carotid artery; the basilar artery was exposed by means of the endoscopic endonasal transclival approach, and the vertebral arteries through the extended endonasal approach to the craniovertebral junction. Possible clinical application of each approach was investigated during anatomical dissections upgraded with imaging and quantification methods. Results. The transtuberculum transplanum approach allows for the exposure and control of the anterior communicating artery complex; the relationship between the proximal anterior cerebral artery, gyrus rectus, and optic chiasm is the main determinant for the exposure and control of the vessel. Temporary occlusion of the internal carotid artery with a Fogarty balloon catheter through the endoscopic transsphenoidal route might be another maneuver that is useful for obtaining intraoperative control of the vessel. The endoscopic transclival approach may be considered a minimally invasive route to the basilar apex in the presence of specific anatomical and pathological features. Comparative analysis of the anatomical exposure of the vertebro-basilar junction as obtained through transcranial and endoscopic endonasal approaches may be helpful in unlocking this complex skull base area. Conclusions. The introduction of the endoscopic endonasal approaches for the treatment of cerebrovascular pathologies represents the most advanced and innovative step forward of the skull base endoscopic endonasal surgical technique. The present PhD research activity may add relevant anatomical and clinical information to the rather sparse literature directly focused on surgical indication of the endoscopic endonasal approaches to vascular neurosurgery

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

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    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

    Radiomic data mining and machine learning on preoperative pituitary adenoma MRI

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    Pituitary adenomas are among the most frequent intracranial tumors, accounting for the majority of sellar/suprasellar masses in adults. MRI is the preferred imaging modality for detecting pituitary adenomas. Radiomics represents the conversion of digital medical images into mineable high-dimensional data. This process is motivated by the concept that biomedical images contain information that reflects underlying pathophysiology and that these relationships can be revealed via quantitative image analyses. The aim of this thesis is to apply machine learning algorithms on parameters obtained by texture analysis on MRI images in order to distinguish functional from non-functional pituitary macroadenomas, to predict their ki-67 proliferation index class, and to predict pituitary macroadenoma surgical consistency prior to an endoscopic endonasal procedure

    A spherical joint robotic end-effector for the Expanded Endoscopic Endonasal Approach

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    The endonasal transsphenoidal approach allows surgeons to access the pituitary gland through the natural orifice of the nose. Recently, surgeons have also described an Expanded Endoscopic Endonasal Approach (EEEA) for the treatment of other tumours around the base of the brain. However, operating in this way with nonarticulated tools is technically very difficult and not widely adopted. The goal of this study is to develop an articulated end-effector for a novel handheld robotic tool for the EEEA. We present a design and implementation of a 3.6mm diameter, three degrees-of-freedom, tendon-driven robotic end-effector that, contrary to rigid instruments which operate under fulcrum, will give the surgeon the ability to reach areas on the surface of the brain that were previously inaccessible. We model the end-effector kinematics in simulation to study the theoretical workspace it can achieve prior to implementing a test-bench device to validate the efficacy of the end-effector. We find promising repeatability of the proposed robotic end-effector of 0.42mm with an effective workspace with limits of ±30∘, which is greater than conventional neurosurgical tools. Additionally, although the tool’s end-effector has a small enough diameter to operate through the narrow nasal access path and the constrained workspace of EEEA, it showcased promising structural integrity and was able to support approximately a 6N load, despite a large deflection angle the limiting of which is scope of future work. These preliminary results indicate the end-effector is a promising first step towards developing appropriate handheld robotic instrumentation to drive EEEA adoption

    Haemostasis in endoscopic skull base surgery

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    The endoscopic approach to the skull base has revolutionised surgery in this region. Neurosurgery involves working around anatomical structures that are uniquely sensitive to damage and manipulation and patients may be left with the potentially devastating consequences of violating these structures. The endoscope allows the surgeon to visualise and reach areas that were previously only accessible with large amounts of destructive dissection. Tumours are able to be removed and aneurysms clipped without the need for large craniotomies and bony drilling. There are, however, drawbacks. The midline endoscopic route takes the surgeon between the carotid arteries. It potentially violates the anterior communicating artery complex and the basilar artery region anterior to the brainstem. These are important arteries that supply critical structures. Damage to these, or diminution of blood flow through them, results in profound neurological dysfunction or death. The rate of damage to the carotid artery with these approaches ranges from 1.1-9% depending on the specific approach and pathology. The carotid artery in this region does not generally lend itself to suturing, clipping or direct closure methods. Currently, the gold standard for repair is the application of crushed muscle patch to stop the bleeding and seal the vessel. The drawbacks to this are that it takes time to harvest and control the bleed (generally requiring 2 surgeons), and that there is a risk of pseudoaneurysm formation post recovery. This thesis describes novel techniques that may replace the muscle patch in order that a single surgeon may have this technique available to them immediately. Aims: To demonstrate the use of fibrin/thrombin/gelatin patches, fibrin/thrombin glues, beta-chitosan patches and self-assembling peptides on a sheep model of carotid artery haemorrhage and quantify the rate of pseudoaneurysm formation. To show the percentage of platelets activated by crushed and uncrushed muscle, chitosan, and fibrin and thrombin patches and gels using flow cytometry to further delineate the mechanism of action of crushed muscle as a haemostatic agent. To quantify the stress response in surgeons training on this sheep vascular haemorrhage model de novo, to quantify its effect on surgeons’ teamwork and communication skills, and determine the effect and value of training on modulation of this stress response.Thesis (Ph.D.) (Research by Publication) -- University of Adelaide, Adelaide Medical School, 201

    Novel concepts and strategies in skull base reconstruction after endoscopic endonasal surgery

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    Recently, a variety of craniofacial approaches has been adopted to enter the skull base, among those, the endonasal endoscopic technique. An effective watertight thereafter: the reconstruction can be performed using different materials, both autologous and non-autologous, individually or combined in a multilayer fashion. The current study was focused on the development of new advanced devices and techniques, aiding in reducing postoperative CSF leak rate. Additive manufacturing allows the design of devices with tailored structural and functional features and, as well, injectable semi-IPNs and composites; therefore specific mechanical/rheological and injectability studies are valuable. Accordingly, we propose new additive-manufactured and injectable devices

    Computational ultrasound tissue characterisation for brain tumour resection

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    In brain tumour resection, it is vital to know where critical neurovascular structuresand tumours are located to minimise surgical injuries and cancer recurrence. Theaim of this thesis was to improve intraoperative guidance during brain tumourresection by integrating both ultrasound standard imaging and elastography in thesurgical workflow. Brain tumour resection requires surgeons to identify the tumourboundaries to preserve healthy brain tissue and prevent cancer recurrence. Thisthesis proposes to use ultrasound elastography in combination with conventionalultrasound B-mode imaging to better characterise tumour tissue during surgery.Ultrasound elastography comprises a set of techniques that measure tissue stiffness,which is a known biomarker of brain tumours. The objectives of the researchreported in this thesis are to implement novel learning-based methods for ultrasoundelastography and to integrate them in an image-guided intervention framework.Accurate and real-time intraoperative estimation of tissue elasticity can guide towardsbetter delineation of brain tumours and improve the outcome of neurosurgery. We firstinvestigated current challenges in quasi-static elastography, which evaluates tissuedeformation (strain) by estimating the displacement between successive ultrasoundframes, acquired before and after applying manual compression. Recent approachesin ultrasound elastography have demonstrated that convolutional neural networkscan capture ultrasound high-frequency content and produce accurate strain estimates.We proposed a new unsupervised deep learning method for strain prediction, wherethe training of the network is driven by a regularised cost function, composed of asimilarity metric and a regularisation term that preserves displacement continuityby directly optimising the strain smoothness. We further improved the accuracy of our method by proposing a recurrent network architecture with convolutional long-short-term memory decoder blocks to improve displacement estimation and spatio-temporal continuity between time series ultrasound frames. We then demonstrateinitial results towards extending our ultrasound displacement estimation method toshear wave elastography, which provides a quantitative estimation of tissue stiffness.Furthermore, this thesis describes the development of an open-source image-guidedintervention platform, specifically designed to combine intra-operative ultrasoundimaging with a neuronavigation system and perform real-time ultrasound tissuecharacterisation. The integration was conducted using commercial hardware andvalidated on an anatomical phantom. Finally, preliminary results on the feasibilityand safety of the use of a novel intraoperative ultrasound probe designed for pituitarysurgery are presented. Prior to the clinical assessment of our image-guided platform,the ability of the ultrasound probe to be used alongside standard surgical equipmentwas demonstrated in 5 pituitary cases
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