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Neuroendoscopy

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Does machine learning improve prediction accuracy of the Endoscopic Third Ventriculostomy Success Score? A contemporary Hydrocephalus Clinical Research Network cohort study.

Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery
PURPOSE: This Hydrocephalus Clinical Research Network (HCRN) study had two aims: (1) to compare the predictive performance of the original ETV Success Score (ETVSS) using logistic regression modeling with other newer machine learning models and (2) t...

Revisiting the Endoscopic Third Ventriculostomy Success Score using machine learning: can we do better?

Journal of neurosurgery. Pediatrics
OBJECTIVE: The Endoscopic Third Ventriculostomy Success Score (ETVSS) is a useful decision-making heuristic when considering the probability of surgical success, defined traditionally as no repeat cerebrospinal fluid diversion surgery needed within 6...

Interactive Surgical Training in Neuroendoscopy: Real-Time Anatomical Feature Localization Using Natural Language Expressions.

IEEE transactions on bio-medical engineering
OBJECTIVE: This study addresses challenges in surgical education, particularly in neuroendoscopy, where the demand for optimized workflow conflicts with the need for trainees' active participation in surgeries. To overcome these challenges, we propos...

Video-Based Performance Analysis in Pituitary Surgery - Part 2: Artificial Intelligence Assisted Surgical Coaching.

World neurosurgery
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 Predictive Model for Intraoperative Cerebrospinal Fluid Leak During Endonasal Pituitary Adenoma Resection Using a Convolutional Neural Network.

World neurosurgery
BACKGROUND: Cerebrospinal fluid (CSF) leak during endoscopic endonasal transsphenoidal surgery can lead to postoperative complications. The clinical and anatomic risk factors of intraoperative CSF leak are not well defined. We applied a two-dimension...

Brain activation in parietal area during manipulation with a surgical robot simulator.

International journal of computer assisted radiology and surgery
PURPOSE: we present an evaluation method to qualify the embodiment caused by the physical difference between master-slave surgical robots by measuring the activation of the intraparietal sulcus in the user's brain activity during surgical robot manip...

Design and Comparison of Magnetically-Actuated Dexterous Forceps Instruments for Neuroendoscopy.

IEEE transactions on bio-medical engineering
Robot-assisted minimally invasive surgical (MIS) techniques offer improved instrument precision and dexterity, reduced patient trauma and risk, and promise to lessen the skill gap among surgeons. These approaches are common in general surgery, urolog...

Investigating the use of a two-stage attention-aware convolutional neural network for the automated diagnosis of otitis media from tympanic membrane images: a prediction model development and validation study.

BMJ open
OBJECTIVES: This study investigated the usefulness and performance of a two-stage attention-aware convolutional neural network (CNN) for the automated diagnosis of otitis media from tympanic membrane (TM) images.

Incorporating New Technologies to Overcome the Limitations of Endoscopic Spine Surgery: Navigation, Robotics, and Visualization.

World neurosurgery
Recently, spine surgery has gradually evolved from conventional open surgery to minimally invasive surgery, and endoscopic spine surgery (ESS) has become an important procedure in minimally invasive spine surgery. With improvements in the optics, spi...

Machine learning-based prediction of outcomes of the endoscopic endonasal approach in Cushing disease: is the future coming?

Neurosurgical focus
OBJECTIVE: Machine learning (ML) is an innovative method to analyze large and complex data sets. The aim of this study was to evaluate the use of ML to identify predictors of early postsurgical and long-term outcomes in patients treated for Cushing d...