A deep learning model for radiological measurement of adolescent idiopathic scoliosis using biplanar radiographs.

Journal: Journal of orthopaedic surgery and research
PMID: 40038733

Abstract

BACKGROUND: Accurate measurement of the spinal alignment parameters is crucial for diagnosing and evaluating adolescent idiopathic scoliosis (AIS). Manual measurement is subjective and time-consuming. The recently developed artificial intelligence models mainly focused on measuring the coronal Cobb angle (CA) and ignored the evaluation of the sagittal plane. We developed a deep-learning model that could automatically measure spinal alignment parameters in biplanar radiographs.

Authors

  • Kunjie Xie
    Department of Orthopaedics, Xijing Hospital, Air Force Medical University, Xi'an, 710032.
  • Suping Zhu
    School of Telecommunications Engineering, Xidian University, Xi'an, 710071.
  • Jincong Lin
    Department of Orthopedics, Xijing Hospital, Air Force Medical University, No.15 Changle Xi Road, Xi'an, 710032, China.
  • Yi Li
    Wuhan Zoncare Bio-Medical Electronics Co., Ltd, Wuhan, China.
  • Jinghui Huang
    Department of Orthopedics, Xijing Hospital, Air Force Military Medical University, Xi'an, 710032, China.
  • Wei Lei
    Department of Orthopaedics, Xijing Hospital, Air Force Medical University, Xi'an, 710032.
  • Yabo Yan
    Department of Orthopedics, Xijing Hospital, Air Force Military Medical University, No.169 Changle West Road, Xi'an, Shaanxi Province, 710032, China.

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