AIMC Topic: Hydrocephalus

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

World neurosurgery Jan 6, 2021
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...
Humans Algorithms Brain Retrospective Studies Young Adult Adult Cross-Sectional Studies Male Middle Aged Tomography, X-Ray Computed Female Aged Aged, 80 and over Datasets as Topic Neuroimaging Skull Atrophy Lateral Ventricles Natural Language Processing Cephalometry Hydrocephalus Models, Biological Deep Learning Adolescent Child, Preschool Child
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Artificial intelligence for automatic cerebral ventricle segmentation and volume calculation: a clinical tool for the evaluation of pediatric hydrocephalus.

Journal of neurosurgery. Pediatrics Dec 1, 2020
OBJECTIVE: Imaging evaluation of the cerebral ventricles is important for clinical decision-making in pediatric hydrocephalus. Although quantitative measurements of ventricular size, over time, can facilitate objective comparison, automated tools for...
Models, Theoretical Magnetic Resonance Imaging Hydrocephalus Cerebral Ventricles Male Infant Deep Learning Adolescent Cohort Studies Child, Preschool Child Image Processing, Computer-Assisted Software Female Infant, Newborn Young Adult Humans Neural Networks, Computer Artificial Intelligence
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Development of machine learning models to prognosticate chronic shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage.

Acta neurochirurgica Jul 8, 2020
BACKGROUND: Shunt-dependent hydrocephalus significantly complicates subarachnoid hemorrhage (SAH), and reliable prognosis methods have been sought in recent years to reduce morbidity and costs associated with delayed treatment or neglected onset. Mac...
Subarachnoid Hemorrhage Retrospective Studies Female Humans Risk Factors Middle Aged Machine Learning Aged Cerebrospinal Fluid Shunts Prognosis Adult Hydrocephalus Male
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Semantic segmentation of cerebrospinal fluid and brain volume with a convolutional neural network in pediatric hydrocephalus-transfer learning from existing algorithms.

Acta neurochirurgica Jun 25, 2020
BACKGROUND: For the segmentation of medical imaging data, a multitude of precise but very specific algorithms exist. In previous studies, we investigated the possibility of segmenting MRI data to determine cerebrospinal fluid and brain volume using a...
Humans Female Male Child Machine Learning Brain Semantics Image Processing, Computer-Assisted Magnetic Resonance Imaging Cerebrospinal Fluid Hydrocephalus
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Comparison of morphometric parameters in prediction of hydrocephalus using random forests.

Computers in biology and medicine Nov 20, 2019
Ventricles of the human brain enlarge with aging, neurodegenerative diseases, intrinsic, and extrinsic pathologies. The morphometric examination of neuroimages is an effective approach to assess structural changes occurring due to diseases such as hy...
Hydrocephalus Image Interpretation, Computer-Assisted Cerebral Ventricles Child, Preschool Child Magnetic Resonance Imaging Decision Trees Adult Male Middle Aged Adolescent Brain Humans Aged Female Machine Learning Aged, 80 and over Algorithms Young Adult
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Automated Ventricular System Segmentation in Paediatric Patients Treated for Hydrocephalus Using Deep Learning Methods.

BioMed research international Jul 7, 2019
Hydrocephalus is a common neurological condition that can have traumatic ramifications and can be lethal without treatment. Nowadays, during therapy radiologists have to spend a vast amount of time assessing the volume of cerebrospinal fluid (CSF) by...
Humans Adolescent Child, Preschool Neural Networks, Computer Child Infant, Newborn Tomography, X-Ray Computed Imaging, Three-Dimensional Hydrocephalus Male Female Infant Deep Learning
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Predicting ventriculoperitoneal shunt infection in children with hydrocephalus using artificial neural network.

Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery Sep 14, 2016
OBJECTIVES: The relationships between shunt infection and predictive factors have not been previously investigated using Artificial Neural Network (ANN) model. The aim of this study was to develop an ANN model to predict shunt infection in a group of...
Child Hydrocephalus Surgical Wound Infection Ventriculoperitoneal Shunt Humans Neural Networks, Computer Female Infant, Newborn Risk Factors Male Infant Child, Preschool
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Deep Learning-based Brain Age Prediction Using MRI to Identify Fetuses with Cerebral Ventriculomegaly.

Radiology. Artificial intelligence Mar 1, 2025
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...
Humans Deep Learning Magnetic Resonance Imaging Hydrocephalus Gestational Age Prenatal Diagnosis Neural Networks, Computer Brain Pregnancy Female
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[[Applications] 13. Segmentation of Infant Brain Ventricles with Hydrocephalus Using Deep Learning].

Nihon Hoshasen Gijutsu Gakkai zasshi Jan 1, 2024
Deep Learning Hydrocephalus Cerebral Ventricles Humans Infant
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Shunt Implants - Past, Present and Future.

Neurology India Jan 1, 2021
BACKGROUND: The treatment of hydrocephalus has evolved over centuries from being an enigma to the use of complex bioprosthetics. Major developments have taken place in the past few decades in shunt hardware and technology, with the use of complex flo...
Hydrocephalus Prostheses and Implants Artificial Intelligence Humans Forecasting Cerebrospinal Fluid Shunts
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