The purpose of the study was to evaluate the association between a quantitative texture analysis of early neonatal brain ultrasound images and later neurobehavior in preterm infants. A prospective cohort study including 120 preterm (<33 wk of gestational age) infants was performed. Cranial ultrasound images taken early after birth were analyzed in six regions of interest using software based on texture analysis. The resulting texture scores were correlated with the Neonatal Behavioural Assessment Scale (NBAS) at term-equivalent age. The ability of texture scores, in combination with clinical data and standard ultrasound findings, to predict the NBAS results was evaluated. Texture scores were significantly associated with all but one NBAS domain and better predicted NBAS results than clinical data and standard ultrasound findings. The best predictive value was obtained by combining texture scores with clinical information and ultrasound standard findings (area under the curve = 0.94). We conclude that texture analysis of neonatal cranial ultrasound-extracted quantitative features that correlate with later neurobehavior has a higher predictive value than the combination of clinical data with abnormalities in conventional cranial ultrasound.
Diagnosis of white matter damage by cranial ultrasound imaging is still subject to interobserver variability and has limited sensitivity for predicting abnormal neurodevelopment later in life. In this study, we evaluated the ability of a semiautomated method based on ultrasound texture analysis to identify patterns that correlate with the ultrasound diagnosis of white matter damage.
To evaluate the feasibility and reproducibility of fetal lung texture analysis using a novel automatic quantitative ultrasound analysis and to assess its correlation with gestational age.
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JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.
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In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.