Clinical workflow of sonographers performing fetal anomaly ultrasound scans: deep-learning-based analysis.

Drukker, L; Sharma, H; Karim, J N; Droste, R; Noble, J A; Papageorghiou, A T

Drukker, L; Sharma, H; Karim, J N; Droste, R; Noble, J A; Papageorghiou, A T.

Afiliação

Drukker L; Nuffield Department of Women's and Reproductive Health, John Radcliffe Hospital, University of Oxford, Oxford, UK.
Sharma H; Women's Ultrasound, Department of Obstetrics and Gynecology, Beilinson Medical Center, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
Karim JN; Institute of Biomedical Engineering, University of Oxford, Oxford, UK.
Droste R; Nuffield Department of Women's and Reproductive Health, John Radcliffe Hospital, University of Oxford, Oxford, UK.
Noble JA; Institute of Biomedical Engineering, University of Oxford, Oxford, UK.
Papageorghiou AT; Institute of Biomedical Engineering, University of Oxford, Oxford, UK.

Ultrasound Obstet Gynecol ; 60(6): 759-765, 2022 12.

Article em En | MEDLINE | ID: mdl-35726505

Assuntos

Aprendizado Profundo; Feminino; Gravidez; Humanos; Fluxo de Trabalho; Ultrassonografia Pré-Natal/métodos; Segundo Trimestre da Gravidez; Feto/anatomia & histologia

Palavras-chave

anatomy; artificial intelligence; automation; big data; clinical workflow; computer vision; data science; deep learning; image analysis; machine learning; neural network; obstetrics; pregnancy; screening; sonography; ultrasound

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Tipo de estudo: Diagnostic_studies / Guideline Limite: Female / Humans / Pregnancy Idioma: En Revista: Ultrasound Obstet Gynecol Assunto da revista: DIAGNOSTICO POR IMAGEM / GINECOLOGIA / OBSTETRICIA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Reino Unido

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