School of Computer Science and Engineering, Chung-Ang University, Seoul, Korea.
Department of Ophthalmology, Chung-Ang University College of Medicine, Chung-Ang University Hospital, 102 Heukseok-ro, Dongjak-gu, Seoul, 06973, Korea.
Sci Rep. 2022 Jul 15;12(1):12071. doi: 10.1038/s41598-022-16217-z.
Computed tomography (CT) has been widely used to diagnose Graves' orbitopathy, and the utility is gradually increasing. To develop a neural network (NN)-based method for diagnosis and severity assessment of Graves' orbitopathy (GO) using orbital CT, a specific type of NN optimized for diagnosing GO was developed and trained using 288 orbital CT scans obtained from patients with mild and moderate-to-severe GO and normal controls. The developed NN was compared with three conventional NNs [GoogleNet Inception v1 (GoogLeNet), 50-layer Deep Residual Learning (ResNet-50), and 16-layer Very Deep Convolutional Network from Visual Geometry group (VGG-16)]. The diagnostic performance was also compared with that of three oculoplastic specialists. The developed NN had an area under receiver operating curve (AUC) of 0.979 for diagnosing patients with moderate-to-severe GO. Receiver operating curve (ROC) analysis yielded AUCs of 0.827 for GoogLeNet, 0.611 for ResNet-50, 0.540 for VGG-16, and 0.975 for the oculoplastic specialists for diagnosing moderate-to-severe GO. For the diagnosis of mild GO, the developed NN yielded an AUC of 0.895, which is better than the performances of the other NNs and oculoplastic specialists. This study may contribute to NN-based interpretation of orbital CTs for diagnosing various orbital diseases.
计算机断层扫描（CT）已广泛用于诊断格雷夫斯眼病（GO），其应用价值逐渐增加。为了开发一种基于神经网络（NN）的方法，利用眼眶 CT 对 GO 进行诊断和严重程度评估，我们开发并训练了一种针对 GO 诊断的特定类型的 NN，该 NN 使用来自轻度和中重度 GO 患者以及正常对照者的 288 例眼眶 CT 扫描进行训练。将开发的 NN 与三种传统的 NN（GoogleNet Inception v1（GoogLeNet）、50 层深度残差学习（ResNet-50）和来自视觉几何组的 16 层深度卷积网络（VGG-16））进行比较。并与三位眼整形专家的诊断表现进行了比较。开发的 NN 对中重度 GO 患者的诊断性能的曲线下面积（AUC）为 0.979。ROC 分析得出 GoogLeNet 的 AUC 为 0.827，ResNet-50 的 AUC 为 0.611，VGG-16 的 AUC 为 0.540，眼整形专家的 AUC 为 0.975。对于轻度 GO 的诊断，开发的 NN 的 AUC 为 0.895，优于其他 NN 和眼整形专家的表现。这项研究可能有助于基于 NN 对眼眶 CT 进行解释，以诊断各种眼眶疾病。
