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-Reference-Cited by-同舟云学术

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

Published:2023 Issue:11 Volume:24 Page:1151
ISSN:1229-6929
Container-title:Korean Journal of Radiology
language:en
Short-container-title:Korean J Radiol

Author:

Kim Pyeong Hwa¹^ORCID,Yoon Hee Mang¹^ORCID,Kim Jeong Rye²^ORCID,Hwang Jae-Yeon³^ORCID,Choi Jin-Ho⁴^ORCID,Hwang Jisun⁵^ORCID,Lee Jaewon⁶^ORCID,Sung Jinkyeong⁶^ORCID,Jung Kyu-Hwan⁶^ORCID,Bae Byeonguk⁶^ORCID,Jung Ah Young¹^ORCID,Cho Young Ah¹^ORCID,Shim Woo Hyun¹⁷^ORCID,Bak Boram⁸^ORCID,Lee Jin Seong¹^ORCID

Affiliation:

1. Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.

2. Department of Radiology, Dankook University Hospital, Dankook University College of Medicine, Cheonan, Republic of Korea.

3. Department of Radiology, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Yangsan, Republic of Korea.

4. Department of Pediatrics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.

5. Department of Radiology, Ajou University Hospital, Ajou University School of Medicine, Suwon, Republic of Korea.

6. VUNO Inc., Seoul, Republic of Korea.

7. Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.

8. University of Ulsan Foundation for Industry Cooperation, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.

Publisher

XMLink

Subject

Radiology, Nuclear Medicine and imaging

Link

https://kjronline.org/pdf/10.3348/kjr.2023.0092

Reference32 articles.

1. Factors associated with Advanced Bone Age in Overweight and Obese Children

2. Diagnosis and constitutional and laboratory features of Korean girls referred for precocious puberty

3. Lower-limb growth: how predictable are predictions?

4. Bone Age: A Handy Tool for Pediatric Providers

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