A self-training deep neural network for early prediction of cognitive deficits in very preterm infants using brain functional connectome data.

Ali, Redha; Li, Hailong; Dillman, Jonathan R; Altaye, Mekibib; Wang, Hui; Parikh, Nehal A; He, Lili

Ali, Redha; Li, Hailong; Dillman, Jonathan R; Altaye, Mekibib; Wang, Hui; Parikh, Nehal A; He, Lili.

Afiliação

Ali R; Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
Li H; Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., MLC 5033, Cincinnati, OH, 45229, USA.
Dillman JR; Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
Altaye M; Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., MLC 5033, Cincinnati, OH, 45229, USA.
Wang H; Center for Artificial Intelligence in Imaging Research, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
Parikh NA; Center for Prevention of Neurodevelopmental Disorders, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
He L; Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.

Pediatr Radiol ; 52(11): 2227-2240, 2022 10.

Article em En | MEDLINE | ID: mdl-36131030

Assuntos

Conectoma; Doenças do Prematuro; Encéfalo/diagnóstico por imagem; Criança; Cognição; Humanos; Lactente; Recém-Nascido; Recém-Nascido Prematuro; Redes Neurais de Computação

Palavras-chave

Cognitive deficit; Deep learning; Deep neural network; Functional connectome; Neonates; Outcome model; Self-training; Semi-supervised learning; Very preterm infants

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Conectoma / Doenças do Prematuro Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Child / Humans / Infant / Newborn Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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