AIMC Topic: Craniotomy
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Classification of speech arrests and speech impairments during awake craniotomy: a multi-databases analysis.
International journal of computer assisted radiology and surgery
Dec 9, 2024
PURPOSE: Awake craniotomy presents a unique opportunity to map and preserve critical brain functions, particularly speech, during tumor resection. The ability to accurately assess linguistic functions in real-time not only enhances surgical precision...
Assessment of Thermal Damage from Robot-Drilled Craniotomy for Cranial Window Surgery in Mice.
Journal of visualized experiments : JoVE
Nov 11, 2022
Cranial window surgery allows for the imaging of brain tissue in live mice with the use of multiphoton or other intravital imaging techniques. However, when performing any craniotomy by hand, there is often thermal damage to brain tissue, which is in...
Three-dimensional deep learning to automatically generate cranial implant geometry.
Scientific reports
Feb 17, 2022
We present a 3D deep learning framework that can generate a complete cranial model using a defective one. The Boolean subtraction between these two models generates the geometry of the implant required for surgical reconstruction. There is little or ...
Machine Learning Approaches-Driven for Mortality Prediction for Patients Undergoing Craniotomy in ICU.
Brain injury
Jan 26, 2022
OBJECTIVES: We aimed to predict the mortality of patients with craniotomy in ICU by using predictive models to extract the high-risk factors leading to the death of patients from a retrospective a study.
Robotic laser osteotomy through penscriptive structured light visual servoing.
International journal of computer assisted radiology and surgery
Feb 7, 2019
PURPOSE: Planning osteotomies is a task that surgeons do as part of standard surgical workflow. This task, however, becomes more difficult and less intuitive when a robot is tasked with performing the osteotomy. In this study, we aim to provide a new...
Healthcare-associated ventriculitis and meningitis in a neuro-ICU: Incidence and risk factors selected by machine learning approach.
Journal of critical care
Mar 23, 2018
PURPOSE: To define the incidence of healthcare-associated ventriculitis and meningitis (HAVM) in the neuro-ICU and to identify HAVM risk factors using tree-based machine learning (ML) algorithms.
High Incidence of Deep Vein Thrombosis in the Perioperative Period of Neurosurgical Patients.
World neurosurgery
Dec 30, 2017
INTRODUCTION: A prospective study was designed to elucidate incidence and predictors of deep venous thrombosis (DVT) in patients undergoing craniotomies.
da Vinci robot-assisted keyhole neurosurgery: a cadaver study on feasibility and safety.
Neurosurgical review
Dec 18, 2014
The goal of this cadaver study was to evaluate the feasibility and safety of da Vinci robot-assisted keyhole neurosurgery. Several keyhole craniotomies were fashioned including supraorbital subfrontal, retrosigmoid and supracerebellar infratentorial....
Machine learning decision support model construction for craniotomy approach of pineal region tumors based on MRI images.
BMC medical imaging
May 27, 2025
BACKGROUND: Pineal region tumors (PRTs) are rare but deep-seated brain tumors, and complete surgical resection is crucial for effective tumor treatment. The choice of surgical approach is often challenging due to the low incidence and deep location. ...
Development of machine learning prediction model for AKI after craniotomy and evacuation of hematoma in craniocerebral trauma.
Medicine
Nov 1, 2024
The aim of this study was to develop a machine-learning prediction model for AKI after craniotomy and evacuation of hematoma in craniocerebral trauma. We included patients who underwent craniotomy and evacuation of hematoma due to traumatic brain inj...
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