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.

Afiliación

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 en En | MEDLINE | ID: mdl-35726505

Asunto(s)

Aprendizaje Profundo; Femenino; Embarazo; Humanos; Flujo de Trabajo; Ultrasonografía Prenatal/métodos; Segundo Trimestre del Embarazo; Feto/anatomía & histología

Palabras clave

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 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo Tipo de estudio: Diagnostic_studies / Guideline Límite: Female / Humans / Pregnancy Idioma: En Revista: Ultrasound Obstet Gynecol Asunto de la revista: DIAGNOSTICO POR IMAGEM / GINECOLOGIA / OBSTETRICIA Año: 2022 Tipo del documento: Article País de afiliación: Reino Unido Pais de publicación: Reino Unido

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