Bone Age Assessment Using Artificial Intelligence in Korean Pediatric Population: A Comparison of Deep-Learning Models Trained With Healthy Chronological and Greulich-Pyle Ages as Labels

OBJECTIVE: To develop a deep-learning-based bone age prediction model optimized for Korean children and adolescents and evaluate its feasibility by comparing it with a Greulich-Pyle-based deep-learning model. MATERIALS AND METHODS: A convolutional neural network was trained to predict age according...

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Detalles Bibliográficos
Autores principales: Kim, Pyeong Hwa, Yoon, Hee Mang, Kim, Jeong Rye, Hwang, Jae-Yeon, Choi, Jin-Ho, Hwang, Jisun, Lee, Jaewon, Sung, Jinkyeong, Jung, Kyu-Hwan, Bae, Byeonguk, Jung, Ah Young, Cho, Young Ah, Shim, Woo Hyun, Bak, Boram, Lee, Jin Seong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Society of Radiology 2023
Materias:
Pediatric Imaging
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10613838/
https://www.ncbi.nlm.nih.gov/pubmed/37899524
http://dx.doi.org/10.3348/kjr.2023.0092

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10613838/
https://www.ncbi.nlm.nih.gov/pubmed/37899524
http://dx.doi.org/10.3348/kjr.2023.0092

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