Neurosurgery

Precise Brain-shift Prediction by New Combination of W-Net Deep Learning for Neurosurgical Navigation.

Brain tissue deformation during surgery significantly reduces the accuracy of image-guided neurosurgeries. We generated updated magnetic resonance images (uMR) in this study to compensate for brain shifts after dural opening using a convolutional neural network (CNN). This study included 248 consecutive patients who underwent craniotomy for initial intra-axial brain tumor removal and corresponding...

Deployment of an electrocorticography system with a soft robotic actuator.

Electrocorticography (ECoG) is a minimally invasive approach frequently used clinically to map epileptogenic regions of the brain and facilitate lesion resection surgery and increasingly explored in brain-machine interface applications. Current devices display limitations that require trade-offs among cortical surface coverage, spatial electrode resolution, aesthetic, and risk consequences and oft...

Editorial. Generative artificial intelligence, chatbots, and the Journal of Neurosurgery Publishing Group.

Development and validation of a deep learning model for prediction of intracranial aneurysm rupture risk based on multi-omics factor.

OBJECTIVE: The clinical ability of radiomics to predict intracranial aneurysm rupture risk remains unexplored. This study aims to investigate the pote...

Identifying fetal status with fetal heart rate: Deep learning approach based on long convolution.

CTG (Cardiotocography) is an effective tool for fetal status assessment. Clinically, doctors mainly evaluate the health of fetus by observing FHR (fet...

Predictors of Remission of Acromegaly following Surgical Treatment in Growth Hormone-Secreting Pituitary Adenoma.

 Surgery is the treatment of choice for growth hormone (GH)-secreting pituitary adenoma. The remission of random GH depends on various factors. We ai...

The role of an open artificial intelligence platform in modern neurosurgical education: a preliminary study.

The use of artificial intelligence in neurosurgical education has been growing in recent times. ChatGPT, a free and easily accessible language model, ...

Combining physics-based models with deep learning image synthesis and uncertainty in intraoperative cone-beam CT of the brain.

BACKGROUND: Image-guided neurosurgery requires high localization and registration accuracy to enable effective treatment and avoid complications. Howe...

Using Machine Learning and Deep Learning Algorithms to Predict Postoperative Outcomes Following Anterior Cervical Discectomy and Fusion.

STUDY DESIGN: A retrospective cohort study from a multisite academic medical center.

Multi-mode information fusion navigation system for robot-assisted vascular interventional surgery.

BACKGROUND: Minimally invasive vascular intervention (MIVI) is a powerful technique for the treatment of cardiovascular diseases, such as abdominal ao...

A Retrospective Study of Puncture and Drainage for Primary Brainstem Hemorrhage With the Assistance of a Surgical Robot.

BACKGROUND: Whether primary brainstem hemorrhage (PBH) should be treated with a conservative treatment or with surgical intervention (such as cranioto...

Segmentation of the aorta in systolic phase from 4D flow MRI: multi-atlas vs. deep learning.

OBJECTIVE: In the management of the aortic aneurysm, 4D flow magnetic resonance Imaging provides valuable information for the computation of new bioma...

Artificial intelligence and the potential for perioperative delabeling of penicillin allergies for neurosurgery inpatients.

PURPOSE OF THE ARTICLE: Patients with penicillin allergy labels are more likely to have postoperative wound infections. When penicillin allergy labels...

High-throughput 3DRA segmentation of brain vasculature and aneurysms using deep learning.

BACKGROUND AND OBJECTIVES: Automatic segmentation of the cerebral vasculature and aneurysms facilitates incidental detection of aneurysms. The assessm...

Robotic Instruments Inside the MRI Bore: Key Concepts and Evolving Paradigms in Imaging-enhanced Cranial Neurosurgery.

Intraoperative MRI has been increasingly used to robotically deliver electrodes and catheters into the human brain using a linear trajectory with grea...

[Robotics and computer-assisted procedures in cranial neurosurgery].

BACKGROUND: The medical technical innovations over the last decade have made operations in the highly sensitive regions of the brain much safer.

Deep learning-based semantic vessel graph extraction for intracranial aneurysm rupture risk management.

PURPOSE: Intracranial aneurysms are vascular deformations in the brain which are complicated to treat. In clinical routines, the risk assessment of in...

A Deep Learning Approach to Visualize Aortic Aneurysm Morphology Without the Use of Intravenous Contrast Agents.

BACKGROUND: Intravenous contrast agents are routinely used in CT imaging to enable the visualization of intravascular pathology, such as with abdomina...

Minimally invasive trans-superior articular process percutaneous endoscopic lumbar discectomy with robot assistance.

BACKGROUND: To compare the clinical outcomes of patients with lumbar disc herniation treated with robot-assisted percutaneous endoscopic lumbar discec...

Deep Learning on Multiphysical Features and Hemodynamic Modeling for Abdominal Aortic Aneurysm Growth Prediction.

Prediction of abdominal aortic aneurysm (AAA) growth is of essential importance for the early treatment and surgical intervention of AAA. Capturing ke...