Development of a Machine Learning Model for Sonographic Assessment of Gestational Age.

Lee, Chace; Willis, Angelica; Chen, Christina; Sieniek, Marcin; Watters, Amber; Stetson, Bethany; Uddin, Akib; Wong, Jonny; Pilgrim, Rory; Chou, Katherine; Tse, Daniel; Shetty, Shravya; Gomes, Ryan G

Lee, Chace; Willis, Angelica; Chen, Christina; Sieniek, Marcin; Watters, Amber; Stetson, Bethany; Uddin, Akib; Wong, Jonny; Pilgrim, Rory; Chou, Katherine; Tse, Daniel; Shetty, Shravya; Gomes, Ryan G.

Afiliação

Lee C; Google Health, Palo Alto, California.
Willis A; Google Health, Palo Alto, California.
Chen C; Google Health, Palo Alto, California.
Sieniek M; Google Health, Palo Alto, California.
Watters A; Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, Illinois.
Stetson B; Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, Illinois.
Uddin A; Google Health, Palo Alto, California.
Wong J; Google Health, Palo Alto, California.
Pilgrim R; Google Health, Palo Alto, California.
Chou K; Google Health, Palo Alto, California.
Tse D; Google Health, Palo Alto, California.
Shetty S; Google Health, Palo Alto, California.
Gomes RG; Google Health, Palo Alto, California.

JAMA Netw Open ; 6(1): e2248685, 2023 01 03.

Article em En | MEDLINE | ID: mdl-36598790

Assuntos

Inteligência Artificial; Aprendizado de Máquina; Humanos; Gravidez; Feminino; Idade Gestacional; Reprodutibilidade dos Testes; Zâmbia; Ultrassonografia

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Aprendizado de Máquina Tipo de estudo: Diagnostic_studies / Guideline / Prognostic_studies Limite: Female / Humans / Pregnancy País/Região como assunto: Africa Idioma: En Revista: JAMA Netw Open Ano de publicação: 2023 Tipo de documento: Article

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