Combining deep learning and intelligent biometry to extract ultrasound standard planes and assess early gestational weeks.

Pei, Yuanyuan; E, Longjiang; Dai, Changping; Han, Jin; Wang, Haiyu; Liang, Huiying

Pei, Yuanyuan; E, Longjiang; Dai, Changping; Han, Jin; Wang, Haiyu; Liang, Huiying.

Afiliação

Pei Y; Clinical Data Center, Guangdong Provincial Clinical Research Center for Child Health, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China.
E L; Clinical Data Center, Guangdong Provincial Clinical Research Center for Child Health, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China.
Dai C; Department of Ultrasonography, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China.
Han J; Prenatal Diagnosis Center of Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China.
Wang H; Department of Ultrasonography, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China. haiyuwang0708@gmail.com.
Liang H; Clinical Data Center, Guangdong Provincial Clinical Research Center for Child Health, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China. lianghuiying803@126.com.

Eur Radiol ; 33(12): 9390-9400, 2023 Dec.

Article em En | MEDLINE | ID: mdl-37392231

Assuntos

Aprendizado Profundo; Gravidez; Feminino; Humanos; Idade Gestacional; Ultrassonografia Pré-Natal; Estudos Retrospectivos; Biometria

Palavras-chave

Biometry; Deep learning; Early pregnancy; Gestational sac; Ultrasound

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Tipo de estudo: Guideline / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Female / Humans / Pregnancy Idioma: En Ano de publicação: 2023 Tipo de documento: Article

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