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.

Many patients with Graves' ophthalmopathy (GO) suffer from dry eye syndrome (DES), and this is one of the most common reasons of eye discomfort in patients with GO. The prevalence of DES in patients with GO is significantly higher than normal subjects. The ocular surface changes involving changes in tears, cornea, conjunctiva and glands occur in GO patients. However, the mechanism of how DES occurs in GO still remains unclear. In this review, the ocular surface changes were illustrated and analyzed the reasons for high prevalence of DES in GO patients.

Orbital blow out fracture is a common disease in emergency department and a delay or failure in diagnosis can lead to permanent visual changes. This study aims to evaluate the ability of an automatic orbital blowout fractures detection system based on computed tomography (CT) data. Orbital CT scans of adult orbital blowout fractures patients and normal cases were obtained from Shanghai Ninth People's Hospital between January and March 2017. The region of fractures was annotated using 3D Slicer. The Inception V3 convolutional neural networks were constructed utilizing the Python programming language with PyTorch as the framework to extract high dimension features from each slice in a CT scan. These extracted features are processed through a XGBoost model to make the final differentiation of fracture cases and nonfracture ones. Accuracy, receiver operating characteristics, and area under the curve were evaluated. This study used 94 CT scans diagnosed with orbital blowout fractures and 94 healthy control cases. The automatic detection system showed accuracy of 92% in single-image classification and 87% in patient level detection. The area under the receiver operating characteristic curve was 0.9574. Using a deep learning-based automatic detection system of orbital blowout fracture can accurately detect and classify orbital blowout fractures from CT scans. The convolutional neural networks model combined with an accurate annotation system could achieve good performance in a small dataset. Further studies with large and multicenter data are required to refine this technology for possible clinical applications.

Introduction Clinically, thyroid-associated ophthalmopathy (TAO) patients were suffered from dry eye syndrome. Only a few relevant studies were about this topic. Our study was determined to provide high-level evidence for the treatment of TAO with dry eye syndrome. Purpose To compare the clinical effects of vitamin A palmitate eye gel and sodium hyaluronate eye drop forTAO patients with dry eye syndrome. Methods The study was conducted in the Ophthalmology Department of the Ninth People’s Hospital Affiliated with the Medical College of Shanghai Jiao Tong University from May to October 2020. A total of 80 mild or moderate-to-severe TAO patients with dry eye syndrome were randomly divided into two groups. The disease stages of all subjects were inactive. Patients in group A were treated with vitamin A palmitate eye gel three times/day for one month and sodium hyaluronate eye drop in group B. The index including break-up time (BUT) and Schirmer I test (ST), corneal fluorescence staining (FL), ocular surface disease index (OSDI), and adverse reactions were recorded by the same clinician at baseline and 1 month after treatment. The data were analyzed by SPSS 24.0. Results Finally, 65 subjects completed the treatment. The average age of the patients in Group A was 38.1 ± 11.4 years, and that in Group B was 37.26 ± 10.67 years. 82% of the subjects in group A were female and 74% in group B. There was no significant difference between the two groups at baseline, including the value of ST, BUT, OSDI, and FL grade. After the treatment, the effective rate was 91.2% in group A, of which the value of BUT and FL grade was significantly improved (P < 0.001). The effective rate in group B was 67.7%, of which the value of OSDI score and FL grade was significantly improved (P = 0.002). In addition, the BUT value of group A was significantly longer than that of group B (P = 0.009). Conclusion InTAO patients with dry eye syndrome, vitamin A palmitate gel and sodium hyaluronate eye drop improved the dry eye and promoted corneal epithelial repair. Vitamin A palmitate gel improves the stability of tear film, while sodium hyaluronate eye drop improves patients’ subjective discomfort.

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Background Computed tomography (CT) can avoid interference factors and has been imported into some software to measure proptosis clinically as the golden standard. Purpose To establish a new method for semi-automatically measuring the proptosis on CT and evaluate its accuracy and reproducibility. Material and Methods A total of 50 orbital CT images were collected of healthy individuals, 25 patients with Graves ophthalmopathy (GO), and 25 patients with orbital fracture (OF). A new image processing software, MedrawHDC, was developed to semi-automatically measure the proptosis (MedrawHDC method). The classic radiological (CR) method (measuring proptosis with the software called Mimics) and MedrawHDC method were applied in all three groups (measured by observer S). Hertel's exophthalmometer (HE) method was also applied in the GO group. Moreover, two other observers were asked to measure the proptosis using MedrawHDC, to evaluate its reproducibility. Results The MedrawHDC method was highly consistent with the CR method in measuring proptosis (normal group: intraclass correlation coefficient [ICC] = 0.989; GO group: ICC = 0.979; OF group: ICC = 0.979). In the GO group, the value of proptosis measured by two radiological methods were consistent with that measured by the HE method (CR method: ICC = 0.703; MedrawHDC method: ICC = 0.697). Bland–Altman plots showed similar results. The measurements obtained by three observers were highly reproducible (ICC = 0.995). Conclusion The newly established MedrawHDC method, with high accessibility, convenience, and repeatability, is reliable in assessing proptosis. It shows high potential for wide application, having clinical value for scientific evaluation of proptosis.

Purpose To compare the surgical outcomes of endoscope-navigation (EN)-assisted orbital decompression and non-EN-assisted orbital decompression for Graves’ orbitopathy (GO) and to assess the potential clinical advantage of EN in orbital decompression surgery. Methods This retrospective cohort study was performed on 227 orbits of 147 GO patients who underwent EN-assisted orbital decompression (185 orbits) or non-EN-assisted orbital decompression (42 orbits). Assessment included proptosis reduction, best-corrected visual acuity (BCVA), diplopia, ocular restriction and surgical complications. Results The proptosis reduction in the EN group was 0.9 mm greater than that in the non-EN group in the entire cohort ( p  =  0.004) and 1.0 mm greater than that in the non-EN group in the propensity score matching cohort ( p  =  0.025) at 2 years postoperatively. In all, 78.2% of orbits with sight-threatening GO in the EN group and 52.6% of orbits in the non-EN group showed BCVA improvement ( p  =  0.026). The proportion of patients with improvement in diplopia was significantly greater in the EN group than in the non-EN group ( p  =  0.026). Conclusions EN offers anatomical localization and deep-seated tissue visualization in orbital decompression and significantly improves the surgical outcomes for GO.