Concordance of Computed Tomography Regional Body Composition Analysis Using a Fully Automated Open-Source Neural Network versus a Reference Semi-Automated Program with Manual Correction.

Gomez-Perez, Sandra L; Zhang, Yanyu; Byrne, Cecily; Wakefield, Connor; Geesey, Thomas; Sclamberg, Joy; Peterson, Sarah

Gomez-Perez, Sandra L; Zhang, Yanyu; Byrne, Cecily; Wakefield, Connor; Geesey, Thomas; Sclamberg, Joy; Peterson, Sarah.

Afiliação

Gomez-Perez SL; Department of Clinical Nutrition, Rush University, Chicago, IL 60612, USA.
Zhang Y; Rush Bioinformatics and Biostatistics Core, Rush University Medical Center, Chicago, IL 60612, USA.
Byrne C; Department of Kinesiology and Nutrition, University of Illinois at Chicago, Chicago, IL 60612, USA.
Wakefield C; Department of Internal Medicine, Brooke Army Medical Center, Fort Sam Houston, TX 78234, USA.
Geesey T; Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, IL 60612, USA.
Sclamberg J; Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, IL 60612, USA.
Peterson S; Department of Clinical Nutrition, Rush University, Chicago, IL 60612, USA.

Sensors (Basel) ; 22(9)2022 Apr 27.

Article em En | MEDLINE | ID: mdl-35591047

Assuntos

Composição Corporal; Tomografia Computadorizada por Raios X; Tecido Adiposo; Índice de Massa Corporal; Feminino; Humanos; Masculino; Redes Neurais de Computação; Tomografia Computadorizada por Raios X/métodos

Palavras-chave

adipose tissue; agreement; artificial intelligence; automated segmentation; body composition; computed tomography; muscle; validation

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Composição Corporal / Tomografia Computadorizada por Raios X Limite: Female / Humans / Male Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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