Bassani Tito, Cina Andrea, Galbusera Fabio, Cazzato Andrea, Pellegrino Maria Elena, Albano Domenico, Sconfienza Luca Maria
IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
Department of Teaching, Research and Development, Schulthess Clinic, Zurich, Switzerland.
Eur Radiol Exp. 2025 Jan 29;9(1):11. doi: 10.1186/s41747-025-00553-6.
Minimizing radiation exposure is crucial in monitoring adolescent idiopathic scoliosis (AIS). Generative adversarial networks (GANs) have emerged as valuable tools being able to generate high-quality synthetic images. This study explores the use of GANs to generate synthetic sagittal radiographs from coronal views in AIS patients.
A dataset of 3,935 AIS patients who underwent spine and pelvis radiographic examinations using the EOS system, which simultaneously acquires coronal and sagittal images, was analyzed. The dataset was divided into training-set (85%, n = 3,356) and test-set (15%, n = 579). GAN model was trained to generate sagittal images from coronal views, with real sagittal views as reference standard. To assess accuracy, 100 subjects from the test-set were randomly selected for manual measurement of lumbar lordosis (LL), sacral slope (SS), pelvic incidence (PI), and sagittal vertical axis (SVA) by two radiologists in both synthetic and real images.
Sixty-nine synthetic images were considered assessable. The intraclass correlation coefficient ranged 0.93-0.99 for measurements in real images, and from 0.83 to 0.88 for synthetic images. Correlations between parameters of real and synthetic images were 0.52 (LL), 0.17 (SS), 0.18 (PI), and 0.74 (SVA). Measurement errors showed minimal correlation with scoliosis severity. Mean ± standard deviation absolute errors were 7 ± 7° (LL), 9 ± 7° (SS), 9 ± 8° (PI), and 1.1 ± 0.8 cm (SVA).
While the model generates sagittal images visually consistent with reference images, their quality is not sufficient for clinical parameter assessment, except for promising results in SVA.
AI can generate synthetic sagittal radiographs from coronal views to reduce radiation exposure in monitoring adolescent idiopathic scoliosis (AIS). However, while these synthetic images appear visually consistent with real ones, their quality remains insufficient for accurate clinical assessment.
AI can be exploited to generate synthetic sagittal radiographs from coronal views. Dataset of 3,935 subjects was used to train and test AI-model; spinal parameters from synthetic and real images were compared. Synthetic images were visually consistent with real ones, but quality was generally insufficient for accurate clinical assessment.
在青少年特发性脊柱侧凸（AIS）的监测中，将辐射暴露降至最低至关重要。生成对抗网络（GANs）已成为能够生成高质量合成图像的有价值工具。本研究探讨了使用GANs从AIS患者的冠状位视图生成矢状位X线片的方法。
分析了3935例接受使用EOS系统进行脊柱和骨盆X线检查的AIS患者的数据集，该系统可同时获取冠状位和矢状位图像。数据集分为训练集（85%，n = 3356）和测试集（15%，n = 579）。训练GAN模型以从冠状位视图生成矢状位图像，以真实矢状位视图作为参考标准。为评估准确性，从测试集中随机选择100名受试者，由两名放射科医生对合成图像和真实图像中的腰椎前凸（LL）、骶骨倾斜度（SS）、骨盆入射角（PI）和矢状垂直轴（SVA）进行手动测量。
69张合成图像被认为可评估。真实图像测量的组内相关系数范围为0.93 - 0.99，合成图像为0.83至0.88。真实图像和合成图像参数之间的相关性分别为0.52（LL）、0.17（SS）、0.18（PI）和0.74（SVA）。测量误差与脊柱侧凸严重程度的相关性最小。平均±标准差绝对误差分别为7±7°（LL）、9±7°（SS）、9±8°（PI）和1.1±0.8 cm（SVA）。
虽然该模型生成的矢状位图像在视觉上与参考图像一致，但其质量不足以用于临床参数评估，除了在SVA方面有较好结果。
人工智能可以从冠状位视图生成合成矢状位X线片，以减少青少年特发性脊柱侧凸（AIS）监测中的辐射暴露。然而，虽然这些合成图像在视觉上与真实图像一致，但其质量仍不足以进行准确的临床评估。
人工智能可用于从冠状位视图生成合成矢状位X线片。使用3935名受试者的数据集训练和测试人工智能模型；比较合成图像和真实图像的脊柱参数。合成图像在视觉上与真实图像一致，但质量通常不足以进行准确的临床评估。
