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- Title
Diagnosis of Scoliosis Using Chest Radiographs with a Semi-Supervised Generative Adversarial Network.
- Authors
Woojin Lee; Keewon Shin; Junsoo Lee; Seung-Jin Yoo; Min A Yoon; Yo Won Choi; Gil-Sun Hong; Namkug Kim; Sanghyun Paik
- 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.
- Subjects
SCOLIOSIS; RADIOGRAPHS
- Publication
Journal of the Korean Society of Radiology (2951-0805), 2022, Vol 83, Issue 6, p1298
- ISSN
2951-0805
- Publication type
Article
- DOI
10.3348/jksr.2021.0146