Your mileage may vary: impact of data input method for a deep learning bone age app's predictions.

Yi, Paul H; Yu, Alice C; Dunn, Emily A; Sair, Haris I

Yi, Paul H; Yu, Alice C; Dunn, Emily A; Sair, Haris I.

Afiliação

Yi PH; University of Maryland Medical Intelligent Imaging (UM2II) Center, Department of Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, USA. pyi@som.umaryland.edu.
Yu AC; The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Dunn EA; The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Sair HI; The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Skeletal Radiol ; 51(2): 423-429, 2022 Feb.

Article em En | MEDLINE | ID: mdl-34476558

Assuntos

Aprendizado Profundo; Aplicativos Móveis; Criança; Humanos; Smartphone

Palavras-chave

Bone age; Deep learning; Smartphone applications; User input

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Aplicativos Móveis / Aprendizado Profundo Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Child / Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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