Height prediction of individuals with osteogenesis imperfecta by machine learning.

Journal: Orphanet journal of rare diseases
PMID: 39521996

Abstract

BACKGROUND: Osteogenesis imperfecta (OI) is a genetic disorder characterized by low bone mass, bone fragility and short stature. There is a significant gap in knowledge regarding the growth patterns across different types of OI, and the prediction of height in individuals with OI was not adequately addressed. In this study, we described the growth patterns and predicted the height of individuals with OI employing multiple machine learning (ML) models. Accurate height prediction enables effective monitoring and facilitates the development of personalized intervention plans for managing OI.

Authors

  • Hongjiang Yang
    Department of Orthopedics, International Science and Technology Cooperation Base of Spinal Cord Injury, Tianjin Key Laboratory of Spine and Spinal Cord Injury, Tianjin Medical University General Hospital, Tianjin, China.
  • Wenbiao Zhu
    Department of Pathology, Meizhou People's Hospital, 514011, Meizhou, China.
  • Bo Li
    Electric Power Research Institute, Yunnan Power Grid Co., Ltd., Kunming, Yunnan, China.
  • Hao Wang
    Department of Cardiology, Second Medical Center, Chinese PLA General Hospital, Beijing, China.
  • Cong Xing
    Department of Orthopedics, International Science and Technology Cooperation Base of Spinal Cord Injury, Tianjin Key Laboratory of Spine and Spinal Cord Injury, Tianjin Medical University General Hospital, Tianjin, China.
  • Yang Xiong
    Andrology Laboratory, West China Hospital, Sichuan University, Chengdu, China.
  • Xiuzhi Ren
    Department of orthopedics, Children's Hospital of Soochow University, Suzhou, China. renxiuzhi7320@suda.edu.cn.
  • Guangzhi Ning
    Department of Orthopedics, International Science and Technology Cooperation Base of Spinal Cord Injury, Tianjin Key Laboratory of Spine and Spinal Cord Injury, Tianjin Medical University General Hospital, Tianjin, China. gzning@tmu.edu.cn.

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