Thyroid-associated ophthalmopathy (TAO) is a complicated orbitopathy related to dysthyroid, which severely destroys the facial appearance and life quality without medical interference. The diagnosis and management of thyroid-associated ophthalmopathy are extremely intricate, as the number of professional ophthalmologists is limited and inadequate compared with the number of patients. Nowadays, medical applications based on artificial intelligence (AI) algorithms have been developed, which have proved effective in screening many chronic eye diseases. The advanced characteristics of automated artificial intelligence devices, such as rapidity, portability, and multi-platform compatibility, have led to significant progress in the early diagnosis and elaborate evaluation of these diseases in clinic. This study aimed to provide an overview of recent artificial intelligence applications in clinical diagnosis, activity and severity grading, and prediction of therapeutic outcomes in thyroid-associated ophthalmopathy. It also discussed the current challenges and future prospects of the development of artificial intelligence applications in treating thyroid-associated ophthalmopathy.
Background Thyroid-associated ophthalmopathy (TAO) is an autoimmune disorder. It has discriminable appearance. This study was conducted to dig the clinical significance of demographic characteristics and ophthalmologic diagram features in TAO diagnosis and stage/severity evaluation. Results We included 320 males and 633 females, with an average age of 41.75 ± 13.75. A majority of TAO patients had hyperthyroidism, and most of them were in the inactive stage and at the moderate level. The thyroid function type, stage and severity were closely associated with hypopsia, eyelid congestion, conjunctival congestion, corneal ulcer, ocular motility disorder, best corrected visual acuity, and extraocular muscle thickening. Using these features, we established different logistic regression models to predict thyroid function subtypes, abnormal thyroid function, stage, and severity, in which the AUC of the ROC curve and accuracies were satisfactory. Conclusion Together, TAO subtype, stage and severity can be diagnosed by auxiliary references including demographic factors, symptoms from complains, and image features. These non-invasive indices can be applied in a timely manner in clinical estimating TAO status.
Graves’ ophthalmopathy (GO) is an inflammatory autoimmune disease that affects the eyes. It can significantly alter the quality of life in patients because of its distinctive pathological appearance and the effect on vision. To date, the exact pathological mechanism of GO has not been explicitly discovered. However, several studies have associated autophagy with this disease. Autophagy is a catabolic process that helps maintain homeostasis in all organisms by protecting the cells and tissues from various endogenous and exogenous stress factors. Based on our results, patients affected with GO have comparatively elevated levels of autophagy, which critically affects the pathological mechanism of the GO. In this review, we have summarized the autophagy mechanism in the pathogenesis of GO.
IgG4‐related disease (IgG4‐RD) is an autoimmune disease involving multiple organs with unique pathological features and has high relapse rates after treatment. Identifying patients at high risk of disease relapse for targeted treatment with long‐term and low‐dose glucocorticoid maintenance therapy is crucial for formulating rational treatment strategies. In this study, we conducted a meta‐analysis of relevant articles to explore the risk factors for IgG4‐RD relapse. We searched the Medline (via PubMed), EMBASE, Web of Science and Cochrane Library databases and extracted the mean and standard deviation of continuous variables and true‐positive (TP), false‐positive (FP), false‐negative (FN) and true‐negative (TN) rates to construct a 2 × 2 contingency table from dichotomous variables, and hazard ratios (HRs) were calculated from univariate and multivariate analyses. Finally, we identified five indicators (elevated baseline serum IgG4 level, eosinophil count, increased multiple organ involvement, history of allergy and proximal bile duct stenosis) as risk factors for IgG4‐RD relapse. These results provide new ideas and directions for more researchers to study the relapse of IgG4‐RD and offer reasonable suggestions for clinicians to select IgG4‐RD patients for low‐dose glucocorticoid maintenance therapy to reduce their relapse rates.
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