Using deep-learning in fetal ultrasound analysis for diagnosis of cystic hygroma in the first trimester

OBJECTIVE: To develop and internally validate a deep-learning algorithm from fetal ultrasound images for the diagnosis of cystic hygromas in the first trimester. METHODS: All first trimester ultrasound scans with a diagnosis of a cystic hygroma between 11 and 14 weeks gestation at our tertiary care...

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Detalles Bibliográficos
Autores principales: Walker, Mark C., Willner, Inbal, Miguel, Olivier X., Murphy, Malia S. Q., El-Chaâr, Darine, Moretti, Felipe, Dingwall Harvey, Alysha L. J., Rennicks White, Ruth, Muldoon, Katherine A., Carrington, André M., Hawken, Steven, Aviv, Richard I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9216531/
https://www.ncbi.nlm.nih.gov/pubmed/35731736
http://dx.doi.org/10.1371/journal.pone.0269323

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9216531/
https://www.ncbi.nlm.nih.gov/pubmed/35731736
http://dx.doi.org/10.1371/journal.pone.0269323

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