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Hydrocephalus

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Applied deep learning in neurosurgery: identifying cerebrospinal fluid (CSF) shunt systems in hydrocephalus patients.

Acta neurochirurgica
BACKGROUND: Over the recent decades, the number of different manufacturers and models of cerebrospinal fluid shunt valves constantly increased. Proper identification of shunt valves on X-ray images is crucial to neurosurgeons and radiologists to deri...

Quantitative noninvasive measurement of cerebrospinal fluid flow in shunted hydrocephalus.

Journal of neurosurgery
OBJECTIVE: Standard MRI protocols lack a quantitative sequence that can be used to evaluate shunt-treated patients with a history of hydrocephalus. The objective of this study was to investigate the use of phase-contrast MRI (PC-MRI), a quantitative ...

Robot-assisted endoscopic third ventriculostomy under intraoperative CT imaging guidance.

Acta neurochirurgica
BACKGROUND: The robot-assisted neurosurgical procedures have recently benefited of the evolution of intraoperative imaging, including mobile CT unit available in the operating room. This facilitated use paved the way to perform more neurosurgical pro...

Infection diagnosis in hydrocephalus CT images: a domain enriched attention learning approach.

Journal of neural engineering
. Hydrocephalus is the leading indication for pediatric neurosurgical care worldwide. Identification of postinfectious hydrocephalus (PIH) verses non-postinfectious hydrocephalus, as well as the pathogen involved in PIH is crucial for developing an a...

Validation of a deep learning model for traumatic brain injury detection and NIRIS grading on non-contrast CT: a multi-reader study with promising results and opportunities for improvement.

Neuroradiology
PURPOSE: This study aimed to assess and externally validate the performance of a deep learning (DL) model for the interpretation of non-contrast computed tomography (NCCT) scans of patients with suspicion of traumatic brain injury (TBI).

Deep Learning-based Brain Age Prediction Using MRI to Identify Fetuses with Cerebral Ventriculomegaly.

Radiology. Artificial intelligence
Fetal ventriculomegaly (VM) and its severity and associated central nervous system (CNS) abnormalities are important indicators of high risk for impaired neurodevelopmental outcomes. Recently, a novel fetal brain age prediction method using a two-dim...

Deep Learning Achieves Neuroradiologist-Level Performance in Detecting Hydrocephalus Requiring Treatment.

Journal of digital imaging
In large clinical centers a small subset of patients present with hydrocephalus that requires surgical treatment. We aimed to develop a screening tool to detect such cases from the head MRI with performance comparable to neuroradiologists. We leverag...

Development of Machine Learning-Based Predictor Algorithm for Conversion of an Ommaya Reservoir to a Permanent Cerebrospinal Fluid Shunt in Preterm Posthemorrhagic Hydrocephalus.

World neurosurgery
BACKGROUND: An Ommaya reservoir can be used to treat posthemorrhagic hydrocephalus secondary to intraventricular hemorrhage of prematurity until an acceptable weight can be obtained to place a permanent shunt. Identifying newborns at higher risk of d...

Automated Lateral Ventricular and Cranial Vault Volume Measurements in 13,851 Patients Using Deep Learning Algorithms.

World neurosurgery
BACKGROUND: No large dataset-derived standard has been established for normal or pathologic human cerebral ventricular and cranial vault volumes. Automated volumetric measurements could be used to assist in diagnosis and follow-up of hydrocephalus or...