Neural network-based method for diagnosis and severity assessment of Graves' orbitopathy using orbital computed tomography.

ABSTRACT: 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

SUBMITTER: Lee J 

PROVIDER: S-EPMC9287334 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature