Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review
Generative artificial intelligence, especially with regard to the generative adversarial network (GAN), is an important research area in radiology as evidenced by a number of literature reviews on the role of GAN in radiology published in the last few years. However, no review article about GAN in p...
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| Format: | Journal Article |
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MDPI
2023
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| Online Access: | http://hdl.handle.net/20.500.11937/92941 |
| _version_ | 1848765681695195136 |
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| author | Ng, Curtise |
| author_facet | Ng, Curtise |
| author_sort | Ng, Curtise |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Generative artificial intelligence, especially with regard to the generative adversarial network (GAN), is an important research area in radiology as evidenced by a number of literature reviews on the role of GAN in radiology published in the last few years. However, no review article about GAN in pediatric radiology has been published yet. The purpose of this paper is to systematically review applications of GAN in pediatric radiology, their performances, and methods for their performance evaluation. Electronic databases were used for a literature search on 6 April 2023. Thirty-seven papers met the selection criteria and were included. This review reveals that the GAN can be applied to magnetic resonance imaging, X-ray, computed tomography, ultrasound and positron emission tomography for image translation, segmentation, reconstruction, quality assessment, synthesis and data augmentation, and disease diagnosis. About 80% of the included studies compared their GAN model performances with those of other approaches and indicated that their GAN models outperformed the others by 0.1–158.6%. However, these study findings should be used with caution because of a number of methodological weaknesses. For future GAN studies, more robust methods will be essential for addressing these issues. Otherwise, this would affect the clinical adoption of the GAN-based applications in pediatric radiology and the potential advantages of GAN could not be realized widely. |
| first_indexed | 2025-11-14T11:39:07Z |
| format | Journal Article |
| id | curtin-20.500.11937-92941 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:39:07Z |
| publishDate | 2023 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-929412023-08-23T02:36:25Z Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review Ng, Curtise computer-aided diagnosis data augmentation deep learning dose reduction image reconstruction image segmentation image translation machine learning medical imaging noise Generative artificial intelligence, especially with regard to the generative adversarial network (GAN), is an important research area in radiology as evidenced by a number of literature reviews on the role of GAN in radiology published in the last few years. However, no review article about GAN in pediatric radiology has been published yet. The purpose of this paper is to systematically review applications of GAN in pediatric radiology, their performances, and methods for their performance evaluation. Electronic databases were used for a literature search on 6 April 2023. Thirty-seven papers met the selection criteria and were included. This review reveals that the GAN can be applied to magnetic resonance imaging, X-ray, computed tomography, ultrasound and positron emission tomography for image translation, segmentation, reconstruction, quality assessment, synthesis and data augmentation, and disease diagnosis. About 80% of the included studies compared their GAN model performances with those of other approaches and indicated that their GAN models outperformed the others by 0.1–158.6%. However, these study findings should be used with caution because of a number of methodological weaknesses. For future GAN studies, more robust methods will be essential for addressing these issues. Otherwise, this would affect the clinical adoption of the GAN-based applications in pediatric radiology and the potential advantages of GAN could not be realized widely. 2023 Journal Article http://hdl.handle.net/20.500.11937/92941 10.3390/children10081372 http://creativecommons.org/licenses/by/4.0/ MDPI fulltext |
| spellingShingle | computer-aided diagnosis data augmentation deep learning dose reduction image reconstruction image segmentation image translation machine learning medical imaging noise Ng, Curtise Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review |
| title | Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review |
| title_full | Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review |
| title_fullStr | Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review |
| title_full_unstemmed | Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review |
| title_short | Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review |
| title_sort | generative adversarial network (generative artificial intelligence) in pediatric radiology: a systematic review |
| topic | computer-aided diagnosis data augmentation deep learning dose reduction image reconstruction image segmentation image translation machine learning medical imaging noise |
| url | http://hdl.handle.net/20.500.11937/92941 |