PDF(Pubmed)
Abstract：
UNASSIGNED: The purpose of this study was to analyze optical coherence tomography (OCT) images of generative adversarial networks (GANs) for the prediction of diabetic macular edema after long-term treatment.
UNASSIGNED: Diabetic macular edema (DME) eyes (n = 327) underwent anti-vascular endothelial growth factor (VEGF) treatments every 4 weeks for 52 weeks from a randomized controlled trial (CRTH258B2305, KINGFISHER) were included. OCT B-scan images through the foveal center at weeks 0, 4, 12, and 52, fundus photography, and retinal thickness (RT) maps were collected. GAN models were trained to generate probable OCT images after treatment. Input for each model were comprised of either the baseline B-scan alone or combined with additional OCT, thickness map, or fundus images. Generated OCT B-scan images were compared with real week 52 images.
UNASSIGNED: For 30 test images, 28, 29, 15, and 30 gradable OCT images were generated by CycleGAN, UNIT, Pix2PixHD, and RegGAN, respectively. In comparison with the real week 52, these GAN models showed positive predictive value (PPV), sensitivity, specificity, and kappa for residual fluid ranging from 0.500 to 0.889, 0.455 to 1.000, 0.357 to 0.857, and 0.537 to 0.929, respectively. For hard exudate (HE), they were ranging from 0.500 to 1.000, 0.545 to 0.900, 0.600 to 1.000, and 0.642 to 0.894, respectively. Models trained with week 4 and 12 B-scans as additional inputs to the baseline B-scan showed improved performance.
UNASSIGNED: GAN models could predict residual fluid and HE after long-term anti-VEGF treatment of DME.
UNASSIGNED: The implementation of this tool may help identify potential nonresponders after long-term treatment, thereby facilitating management planning for these eyes.
UNASSIGNED: Diabetic macular edema (DME) eyes (n = 327) underwent anti-vascular endothelial growth factor (VEGF) treatments every 4 weeks for 52 weeks from a randomized controlled trial (CRTH258B2305, KINGFISHER) were included. OCT B-scan images through the foveal center at weeks 0, 4, 12, and 52, fundus photography, and retinal thickness (RT) maps were collected. GAN models were trained to generate probable OCT images after treatment. Input for each model were comprised of either the baseline B-scan alone or combined with additional OCT, thickness map, or fundus images. Generated OCT B-scan images were compared with real week 52 images.
UNASSIGNED: For 30 test images, 28, 29, 15, and 30 gradable OCT images were generated by CycleGAN, UNIT, Pix2PixHD, and RegGAN, respectively. In comparison with the real week 52, these GAN models showed positive predictive value (PPV), sensitivity, specificity, and kappa for residual fluid ranging from 0.500 to 0.889, 0.455 to 1.000, 0.357 to 0.857, and 0.537 to 0.929, respectively. For hard exudate (HE), they were ranging from 0.500 to 1.000, 0.545 to 0.900, 0.600 to 1.000, and 0.642 to 0.894, respectively. Models trained with week 4 and 12 B-scans as additional inputs to the baseline B-scan showed improved performance.
UNASSIGNED: GAN models could predict residual fluid and HE after long-term anti-VEGF treatment of DME.
UNASSIGNED: The implementation of this tool may help identify potential nonresponders after long-term treatment, thereby facilitating management planning for these eyes.
摘要：
■这项研究的目的是分析生成对抗网络（GAN）的光学相干断层扫描（OCT）图像，以预测长期治疗后的糖尿病性黄斑水肿。
■糖尿病性黄斑水肿（DME）眼（n=327）每4周接受抗血管内皮生长因子（VEGF）治疗，共52周。在第0、4、12和52周，通过中央凹中心的OCTB扫描图像，眼底摄影，收集视网膜厚度（RT）图。训练GAN模型以在治疗后生成可能的OCT图像。每个模型的输入包括单独的基线B扫描或与额外的OCT组合。厚度图,或眼底图像。将生成的OCTB扫描图像与实际的52周图像进行比较。
■对于30个测试图像，CycleGAN生成了28、29、15和30张可分级的OCT图像，UNIT,Pix2PixHD,还有RegGAN,分别。与真实的第52周相比，这些GAN模型显示出阳性预测值(PPV)，灵敏度,特异性，残留流体的κ分别为0.500至0.889、0.455至1.000、0.357至0.857和0.537至0.929。对于硬渗出物（HE），它们分别为0.500至1.000、0.545至0.900、0.600至1.000和0.642至0.894。用第4周和12周B扫描作为基线B扫描的额外输入训练的模型显示出改善的性能。
■GAN模型可以预测DME长期抗VEGF治疗后的残余液体和HE。
■该工具的实施可能有助于识别长期治疗后潜在的无反应者，从而促进这些眼睛的管理规划。
■糖尿病性黄斑水肿（DME）眼（n=327）每4周接受抗血管内皮生长因子（VEGF）治疗，共52周。在第0、4、12和52周，通过中央凹中心的OCTB扫描图像，眼底摄影，收集视网膜厚度（RT）图。训练GAN模型以在治疗后生成可能的OCT图像。每个模型的输入包括单独的基线B扫描或与额外的OCT组合。厚度图,或眼底图像。将生成的OCTB扫描图像与实际的52周图像进行比较。
■对于30个测试图像，CycleGAN生成了28、29、15和30张可分级的OCT图像，UNIT,Pix2PixHD,还有RegGAN,分别。与真实的第52周相比，这些GAN模型显示出阳性预测值(PPV)，灵敏度,特异性，残留流体的κ分别为0.500至0.889、0.455至1.000、0.357至0.857和0.537至0.929。对于硬渗出物（HE），它们分别为0.500至1.000、0.545至0.900、0.600至1.000和0.642至0.894。用第4周和12周B扫描作为基线B扫描的额外输入训练的模型显示出改善的性能。
■GAN模型可以预测DME长期抗VEGF治疗后的残余液体和HE。
■该工具的实施可能有助于识别长期治疗后潜在的无反应者，从而促进这些眼睛的管理规划。
