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

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          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.

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          Author and article information

          Journal
          Comput Methods Programs Biomed
          Computer methods and programs in biomedicine
          Elsevier BV
          1872-7565
          0169-2607
          Dec 2020
          : 197
          Affiliations
          [1 ] Department of Orthopaedic Surgery, International St. Mary's Hospital, Catholic Kwandong University College of Medicine, Incheon, Republic of Korea.
          [2 ] Department of Mechanical Design Engineering, Hanyang University, 222, Wangsimni-ro, Seongdongsu, Seoul 04763, Republic of Korea.
          [3 ] Department of Mechanical Engineering, Korea National University of Transportation, 50 Daehak-ro, Chungju, Chungcheongbuk-do 380-702, Republic of Korea.
          [4 ] Department of Medicine, Catholic Kwandong Graduate School, 24, Beomil-ro, 579 Beon-gil, Gangneung-si, Gangwon-do, 25601, Republic of Korea.
          [5 ] Department of Mechanical Engineering, Hanyang University, 55, Hanyangdaehak-ro, Sangnok-gu, Ansan-si, Gyeonggi-do, 15588, Republic of Korea. Electronic address: yooncsmd@gmail.com.
          Article
          S0169-2607(20)31532-7
          10.1016/j.cmpb.2020.105699
          32805697
          ff3c3d44-fc3a-46cb-b529-d5b415746495
          History

          Artificial intelligent,Convolutional neural network,Orthopaedic,Radiology,Spinopelvic

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