Background This study aimed to establish a deep learning system for detecting the active and inactive phases of thyroid-associated ophthalmopathy (TAO) using magnetic resonance imaging (MRI). This system could provide faster, more accurate, and more objective assessments across populations. Methods A total of 160 MRI images of patients with TAO, who visited the Ophthalmology Clinic of the Ninth People’s Hospital, were retrospectively obtained for this study. Of these, 80% were used for training and validation, and 20% were used for testing. The deep learning system, based on deep convolutional neural network, was established to distinguish patients with active phase from those with inactive phase. The accuracy, precision, sensitivity, specificity, F1 score and area under the receiver operating characteristic curve were analyzed. Besides, visualization method was applied to explain the operation of the networks. Results Network A inherited from Visual Geometry Group network. The accuracy, specificity and sensitivity were 0.863±0.055, 0.896±0.042 and 0.750±0.136 respectively. Due to the recurring phenomenon of vanishing gradient during the training process of network A, we added parts of Residual Neural Network to build network B. After modification, network B improved the sensitivity (0.821±0.021) while maintaining a good accuracy (0.855±0.018) and a good specificity (0.865±0.021). Conclusions The deep convolutional neural network could automatically detect the activity of TAO from MRI images with strong robustness, less subjective judgment, and less measurement error. This system could standardize the diagnostic process and speed up the treatment decision making for TAO.
Glaucoma, a neurodegenerative disease that leads to irreversible vision loss, is characterized by progressive loss of retinal ganglion cells (RGCs) and optic axons. To date, elevated intraocular pressure (IOP) has been recognized as the main phenotypic factor associated with glaucoma. However, some patients with normal IOP also have glaucomatous visual impairment and RGC loss. Unfortunately, the underlying mechanisms behind such cases remain unclear. Recent studies have suggested that retinal glia play significant roles in the initiation and progression of glaucoma. Multiple types of glial cells are activated in glaucoma. Microglia, for example, act as critical mediators that orchestrate the progression of neuroinflammation through pro-inflammatory cytokines. In contrast, macroglia (astrocytes and Müller cells) participate in retinal inflammatory responses as modulators and contribute to neuroprotection through the secretion of neurotrophic factors. Notably, research results have indicated that intricate interactions between microglia and macroglia might provide potential therapeutic targets for the prevention and treatment of glaucoma. In this review, we examine the specific roles of microglia and macroglia in open-angle glaucoma, including glaucoma in animal models, and analyze the interaction between these two cell types. In addition, we discuss potential treatment options based on the relationship between glial cells and neurons.
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