PDF(Pubmed)
Abstract：
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.
摘要：
生成人工智能，特别是关于生成对抗网络(GAN)，是放射学的重要研究领域，最近几年发表的有关GAN在放射学中的作用的许多文献综述证明了这一点。然而,尚未发表关于GAN在儿科放射学中的评论文章.本文的目的是系统地回顾GAN在儿科放射学中的应用。他们的表演,以及他们的业绩评价方法。电子数据库于2023年4月6日用于文献检索。37篇论文符合选择标准并被纳入。这篇综述揭示了GAN可以应用于磁共振成像，X光片,计算机断层扫描,用于图像平移的超声和正电子发射断层扫描，分割，重建,质量评估，综合和数据增强，和疾病诊断。大约80％的纳入研究将他们的GAN模型性能与其他方法的性能进行了比较，并表明他们的GAN模型优于其他方法0.1-158.6％。然而,这些研究结果应谨慎使用，因为一些方法学上的弱点。对于未来的GAN研究，更强大的方法对于解决这些问题至关重要。否则,这将影响基于GAN的儿科放射学应用的临床采用,GAN的潜在优势无法得到广泛认识.
