Clinical Validation of a Deep Learning-Based Hybrid (Greulich-Pyle and Modified Tanner-Whitehouse) Method for Bone Age Assessment.

Journal: Korean journal of radiology
Published Date: Dec 1, 2021

Abstract

OBJECTIVE: To evaluate the accuracy and clinical efficacy of a hybrid Greulich-Pyle (GP) and modified Tanner-Whitehouse (TW) artificial intelligence (AI) model for bone age assessment.

Authors

  • Kyu-Chong Lee
    Department of Radiology, Korea University Anam Hospital, Seoul, Korea.
  • Kee-Hyoung Lee
    Korea University Anam Hospital, Seoul, South Korea.
  • Chang Ho Kang
    Department of Radiology, Korea University Anam Hospital, Seoul, Korea. mallecot@gmail.com.
  • Kyung-Sik Ahn
    Department of Radiology, Korea University Anam Hospital, Seoul, Korea.
  • Lindsey Yoojin Chung
    Department of Pediatrics, Myongji Hospital, Goyang, Korea.
  • Jae-Joon Lee
    Department of Food and Nutrition, College of Natural Science, Chosun University, Gwangju 501-759, Korea.
  • Suk Joo Hong
    Department of Radiology, Korea University Guro Hospital, Seoul, Korea.
  • Baek Hyun Kim
    Department of Radiology, Korea University Ansan Hospital, Ansan, Korea.
  • Euddeum Shim
    Department of Radiology, Korea University Ansan Hospital, Ansan, Korea.

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