Automated quantitative evaluation of brain MRI may be more accurate for discriminating preterm born adults.
Alina JurcoaneMarcel DaamenVera C KeilLukas ScheefJosef G BäumlChun MengAfra M WohlschlägerChristian SorgBarbara BuschNicole BaumannDieter WolkePeter BartmannHenning BoeckerGuido LüchtersMilka MarinovaElke HattingenPublished in: European radiology (2019)
• Our study confirms prior reports showing that structural brain abnormalities related to preterm birth persist into adulthood. • In the clinical practice, if large and deformed lateral ventricles, small and thin corpus callosum, and "dirty" white matter are visible on MRI, ask for prematurity before considering other diagnoses. • Although prevalent, visual findings have low accuracy; adding automatic segmentation of lateral ventricles and deep gray matter nuclei improves the diagnostic accuracy.
Keyphrases
- preterm birth
- low birth weight
- white matter
- gestational age
- deep learning
- preterm infants
- contrast enhanced
- clinical practice
- multiple sclerosis
- magnetic resonance imaging
- resting state
- machine learning
- high resolution
- minimally invasive
- diffusion weighted imaging
- convolutional neural network
- functional connectivity
- depressive symptoms
- computed tomography
- magnetic resonance
- blood brain barrier
- early life
- brain injury