Machine learning and the prediction of hydrocephalus can quantitative image analysis assist the clinician?

Journal article

Peer reviewed

Peter A. Chiarelli, Jason S. Hauptman and Samuel R. Browd

JAMA Pediatrics, Vol.172(2)

2018

Abstract

Female

Humans

Hydrocephalus

Machine Learning

Magnetic Resonance Spectroscopy

Pregnancy

Prenatal Care

diagnostic accuracy

Editorial

human

hydrocephalus

image analysis

lateral brain ventricle

machine learning

mathematical model

neuroimaging

nuclear magnetic resonance imaging

predictive value

priority journal

prognosis

quantitative analysis

sensitivity and specificity

support vector machine

female

hydrocephalus

nuclear magnetic resonance spectroscopy

pregnancy

prenatal care

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Title: Machine learning and the prediction of hydrocephalus can quantitative image analysis assist the clinician?
Creators: Peter A. Chiarelli - Seattle Children's Hospital
Jason S. Hauptman - Seattle Children's Hospital
Samuel R. Browd - Seattle Children's Hospital
Publication Details: JAMA Pediatrics, Vol.172(2)
Publisher: American Medical Association
Identifiers: 991006161506902656
Language: English
Resource Type: Journal article