Application of deep-learning–based artificial intelligence in acetabular index measurement

OBJECTIVE: To construct an artificial intelligence system to measure acetabular index and evaluate its accuracy in clinical application. METHODS: A total of 10,219 standard anteroposterior pelvic radiographs were collected retrospectively from April 2014 to December 2018 in our hospital. Of these, 9...

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Detalles Bibliográficos
Autores principales: Wu, Qingjie, Ma, Hailong, Sun, Jun, Liu, Chuanbin, Fang, Jihong, Xie, Hongtao, Zhang, Sicheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Pediatrics
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9891291/
https://www.ncbi.nlm.nih.gov/pubmed/36741093
http://dx.doi.org/10.3389/fped.2022.1049575

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9891291/
https://www.ncbi.nlm.nih.gov/pubmed/36741093
http://dx.doi.org/10.3389/fped.2022.1049575

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