J Korean Soc Radiol.
2022 Nov;83(6):1298-1311. 10.3348/jksr.2021.0146.
Diagnosis of Scoliosis Using Chest Radiographs with a Semi-Supervised Generative Adversarial Network
- Affiliations
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- 1Department of Radiology, Hanyang University Hospital, Seoul, Korea
- 2Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
- 3Department of Industrial Engineering, Seoul National University, Seoul, Korea
- 4Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
- 5Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
- KMID: 2536818
- DOI: http://doi.org/10.3348/jksr.2021.0146
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.
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