Journal of the Korean Society of Radiology (대한영상의학회지)

The Korean Society of Radiology (대한영상의학회)
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Diagnosis of Scoliosis Using Chest Radiographs with a Semi-Supervised Generative Adversarial Network

준지도학습 방법을 이용한 흉부 X선 사진에서 척추측만증의 진단

  • Woojin Lee (Department of Radiology, Hanyang University Hospital) ;
  • Keewon Shin (Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Junsoo Lee (Department of Industrial Engineering, Seoul National University) ;
  • Seung-Jin Yoo (Department of Radiology, Hanyang University Hospital) ;
  • Min A Yoon (Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center) ;
  • Yo Won Choi (Department of Radiology, Hanyang University Hospital) ;
  • Gil-Sun Hong (Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center) ;
  • Namkug Kim (Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Sanghyun Paik (Department of Radiology, Hanyang University Hospital)
  • 이우진 (한양대학교병원 영상의학과) ;
  • 신기원 (울산대학교 의과대학 서울아산병원 아산융합의학원 의공학과) ;
  • 이준수 (서울대학교 산업공학과) ;
  • 유승진 (한양대학교병원 영상의학과) ;
  • 윤민아 (울산대학교 의과대학 서울아산병원 영상의학과) ;
  • 최요원 (한양대학교병원 영상의학과) ;
  • 홍길선 (울산대학교 의과대학 서울아산병원 영상의학과) ;
  • 김남국 (울산대학교 의과대학 서울아산병원 아산융합의학원 의공학과) ;
  • 백상현 (한양대학교병원 영상의학과)
  • Received : 2021.08.27
  • Accepted : 2021.11.08
  • Published : 2022.11.01
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Abstract

Purpose To develop and validate a deep learning-based screening tool for the early diagnosis of scoliosis using chest radiographs with a semi-supervised generative adversarial network (GAN). Materials and Methods Using a semi-supervised learning framework with a GAN, a screening tool for diagnosing scoliosis was developed and validated through the chest PA radiographs of patients at two different tertiary hospitals. Our proposed method used training GAN with mild to severe scoliosis only in a semi-supervised manner, as an upstream task to learn scoliosis representations and a downstream task to perform simple classification for differentiating between normal and scoliosis states sensitively. Results The area under the receiver operating characteristic curve, negative predictive value (NPV), positive predictive value, sensitivity, and specificity were 0.856, 0.950, 0.579, 0.985, and 0.285, respectively. Conclusion Our deep learning-based artificial intelligence software in a semi-supervised manner achieved excellent performance in diagnosing scoliosis using the chest PA radiographs of young individuals; thus, it could be used as a screening tool with high NPV and sensitivity and reduce the burden on radiologists for diagnosing scoliosis through health screening chest radiographs.

목적 흉부 X선 사진에서 척추측만증을 조기진단 할 수 있는 딥러닝 기반의 스크리닝 소프트웨어를 준지도학습(semi-supervised generative adversarial network; 이하 GAN) 방법을 이용하여 개발하고자 하였다. 대상과 방법 두 곳의 상급종합병원에서 촬영된 흉부 X선 사진에서 척추측만증을 조기진단할 수 있는 스크리닝 소프트웨어를 개발하기 위하여 GAN 방법이 이용되었다. GAN의 훈련과정에서 경증에서 중증의 척추측만증을 보이는 흉부 X선 사진들을 사용하였으며 upstream task에서 척추측만증의 특징을 학습하고, downstream task에서 정상과 척추측만증을 분류하도록 훈련하였다. 결과 수신자 조작 특성 곡선의 곡선하면적(area under the receiver operating characteristic curve), 음성예측도, 양성예측도, 민감도 및 특이도는 각각 0.856, 0.950, 0.579, 0.985, 0.285이었다. 결론 우리가 GAN 방법을 이용하여 개발한 딥러닝 기반의 스크리닝 소프트웨어는 청소년의 흉부 X선에서 척추측만증을 진단하는데 있어서 높은 음성예측도와 민감도를 보였다. 이 소프트웨어가 건강검진을 목적으로 촬영한 청소년의 흉부 X선 사진에 진단 스크리닝 도구로써 이용된다면 영상의학과 의사의 부담을 덜어주며, 척추측만증의 조기진단에 기여할 것으로 생각된다.

Keywords

Acknowledgement

We would like to thank Editage (www.editage.co.kr) for English language editing.

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