Neurosurgery

The Effectiveness and Safety of Ultrasonic Bone Scalpel Versus Conventional Method in Cervical Laminectomy: A Retrospective Study of 311 Patients.

STUDY DESIGN: Retrospective cohort study.

Comparison of statistical learning approaches for cerebral aneurysm rupture assessment.

PURPOSE: Incidental aneurysms pose a challenge to physicians who need to decide whether or not to treat them. A statistical model could potentially support such treatment decisions. The aim of this study was to compare a previously developed aneurysm rupture logistic regression probability model (LRM) to other machine learning (ML) classifiers for discrimination of aneurysm rupture status.

Automatic radiomic feature extraction using deep learning for angiographic parametric imaging of intracranial aneurysms.

BACKGROUND: Angiographic parametric imaging (API) is an imaging method that uses digital subtraction angiography (DSA) to characterize contrast media ...

Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review.

Machine learning (ML) involves algorithms learning patterns in large, complex datasets to predict and classify. Algorithms include neural networks (NN...

Learning Curve of Microendoscopic Discectomy in Single-Level Prolapsed Intervertebral Disc in 120 Patients.

STUDY DESIGN: Retrospective study of prospectively collected data.

Prediction of Aneurysm Stability Using a Machine Learning Model Based on PyRadiomics-Derived Morphological Features.

Background and Purpose- Discrimination of the stability of intracranial aneurysms is critical for determining the treatment strategy, especially in sm...

Machine Learning Models can Detect Aneurysm Rupture and Identify Clinical Features Associated with Rupture.

BACKGROUND: Machine learning (ML) has been increasingly used in medicine and neurosurgery. We sought to determine whether ML models can distinguish ru...

HALS, EVAR and robot-assisted surgery as minimally invasive approaches for abdominal aneurysm treatment.

Automatic Segmentation, Detection, and Diagnosis of Abdominal Aortic Aneurysm (AAA) Using Convolutional Neural Networks and Hough Circles Algorithm.

PURPOSE: An abdominal aortic aneurysm (AAA) is known as a cardiovascular disease involving localized deformation (swelling or enlargement) of aorta oc...

Real-time estimation of electric fields induced by transcranial magnetic stimulation with deep neural networks.

BACKGROUND: Transcranial magnetic stimulation (TMS) plays an important role in treatment of mental and neurological illnesses, and neurosurgery. Howev...

Transforaminal Posterior Approach Is Effective for Treatment of Lower Thoracic Spine Spondylodiscitis.

BACKGROUND: Patients with lower-thoracic spine pathologies that affect anterior column stability and compress the neural tissues need anterior decompr...

Machine Learning Analysis of Matricellular Proteins and Clinical Variables for Early Prediction of Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage.

Although delayed cerebral ischemia (DCI) is a well-known complication after subarachnoid hemorrhage (SAH), there are no reliable biomarkers to predict...

The Adaptive Hermite Fractal Tree (AHFT): a novel surgical 3D path planning approach with curvature and heading constraints.

PURPOSE: In the context of minimally invasive neurosurgery, steerable needles such as the one developed within the Horizon2020-funded EDEN2020 project...

Machine learning for prediction of sustained opioid prescription after anterior cervical discectomy and fusion.

BACKGROUND CONTEXT: The severity of the opioid epidemic has increased scrutiny of opioid prescribing practices. Spine surgery is a high-risk episode f...

Stereotactic brain biopsy: evaluation of robot-assisted procedure in 60 patients.

BACKGROUND: Frameless stereotactic biopsies, particularly robot-assisted procedures are increasing in neurosurgery centers. Results of these procedure...

Deep Learning-Based Detection of Intracranial Aneurysms in 3D TOF-MRA.

BACKGROUND AND PURPOSE: The rupture of an intracranial aneurysm is a serious incident, causing subarachnoid hemorrhage associated with high fatality a...

PENYEK: Automated brown planthopper detection from imperfect sticky pad images using deep convolutional neural network.

Rice is a staple food in Asia and it contributes significantly to the Gross Domestic Product (GDP) of Malaysia and other developing countries. Brown P...

Machine learning modeling for predicting hospital readmission following lumbar laminectomy.

In BriefAuthors of this study analyzed hospital readmissions following laminectomy and developed predictive models to identify readmitted patients wit...

Deep learning-based preoperative predictive analytics for patient-reported outcomes following lumbar discectomy: feasibility of center-specific modeling.

BACKGROUND CONTEXT: There is considerable variability in patient-reported outcome measures following surgery for lumbar disc herniation. Individualize...

Use of artificial neural networks to predict anterior communicating artery aneurysm rupture: possible methodological considerations.

Use of algorithms to generate synthetic cases might result in a misrepresentation of the entire population. Training an artificial neural network with...