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Decentralized convolutional neural network for evaluating spinal deformity with spinopelvic parameters.

Authors
  • Chae, Dong-Sik1
  • Nguyen, Thong Phi2
  • Park, Sung-Jun3
  • Kang, Kyung-Yil4
  • Won, Chanhee2
  • Yoon, Jonghun5
  • 1 Department of Orthopaedic Surgery, International St. Mary's Hospital, Catholic Kwandong University College of Medicine, Incheon, Republic of Korea. , (North Korea)
  • 2 Department of Mechanical Design Engineering, Hanyang University, 222, Wangsimni-ro, Seongdongsu, Seoul 04763, Republic of Korea. , (North Korea)
  • 3 Department of Mechanical Engineering, Korea National University of Transportation, 50 Daehak-ro, Chungju, Chungcheongbuk-do 380-702, Republic of Korea. , (North Korea)
  • 4 Department of Medicine, Catholic Kwandong Graduate School, 24, Beomil-ro, 579 Beon-gil, Gangneung-si, Gangwon-do, 25601, Republic of Korea. , (North Korea)
  • 5 Department of Mechanical Engineering, Hanyang University, 55, Hanyangdaehak-ro, Sangnok-gu, Ansan-si, Gyeonggi-do, 15588, Republic of Korea. Electronic address: [email protected] , (North Korea)
Type
Published Article
Journal
Computer methods and programs in biomedicine
Publication Date
Aug 09, 2020
Volume
197
Pages
105699–105699
Identifiers
DOI: 10.1016/j.cmpb.2020.105699
PMID: 32805697
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Low back pain which is caused by the abnormal spinal alignment is one of the most common musculoskeletal symptom and, consequently, is the reason for not only reduction of productivity but also personal suffering. In clinical diagnosis for this disease, estimating adult spinal deformity is required as an indispensable procedure in highlighting abnormal values to output timely warnings and providing precise geometry dimensions for therapeutic therapies. This paper presents an automated method for precisely measuring spinopelvic parameters using a decentralized convolutional neural network as an efficient replacement for current manual process which not only requires experienced surgeons but also shows limitation in ability to process large numbers of images to accommodate the explosion of big data technologies. The proposed method is based on gradually narrowing the regions of interest (ROIs) for feature extraction and leads the model to mainly focus on the necessary geometry characteristics represented as keypoints. According to keypoints obtained, parameters representing the spinal deformity are calculated, which consistency with manual measurement was validated by 40 test cases and, potentially, provided 1.45o mean absolute values of deviation for PTA as the minimum and 3.51o in case of LSA as maximum. Copyright © 2020. Published by Elsevier B.V.

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