Exploration on OCT biomarker candidate related to macular edema caused by diabetic retinopathy and retinal vein occlusion in SD-OCT images.

To improve the understanding of potential pathological mechanisms of macular edema (ME), we try to discover biomarker candidates related to ME caused by diabetic retinopathy (DR) and retinal vein occlusion (RVO) in spectral-domain optical coherence tomography images by means of deep learning (DL). 32 eyes of 26 subjects with non-proliferative DR (NPDR), 77 eyes of 61 subjects with proliferative DR (PDR), 120 eyes of 116 subjects with branch RVO (BRVO), and 17 eyes of 15 subjects with central RVO (CRVO) were collected. A DL model was implemented to guide biomarker candidate discovery. The disorganization of the retinal outer layers (DROL), i.e., the gray value of the retinal tissues between the external limiting membrane (ELM) and retinal pigment epithelium (RPE), the disrupted and obscured rate of the ELM, ellipsoid zone (EZ), and RPE, was measured. In addition, the occurrence, number, volume, and projected area of hyperreflective foci (HRF) were recorded. ELM, EZ, and RPE are more likely to be obscured in RVO group and HRFs are observed more frequently in DR group (all P ≤ 0.001). In conclusion, the features of DROL and HRF can be possible biomarkers related to ME caused by DR and RVO in OCT modality.

Controllable editing via diffusion inversion on ultra-widefield fluorescein angiography for the comprehensive analysis of diabetic retinopathy.

By incorporating multiple indicators that facilitate clinical decision making and effective management of diabetic retinopathy (DR), a comprehensive understanding of the progression of the disease can be achieved. However, the diversity of DR complications poses challenges to the automatic analysis of various information within images. This study aims to establish a deep learning system designed to examine various metrics linked to DR in ultra-widefield fluorescein angiography (UWFA) images. We have developed a unified model based on image generation that transforms input images into corresponding disease-free versions. By incorporating an image-level supervised training process, the model significantly reduces the need for extensive manual involvement in clinical applications. Furthermore, compared to other comparative methods, the quality of our generated images is significantly superior.

Risk Factors and Management of Intraocular Pressure Elevation After Vitrectomy Combined with Silicone Oil Tamponade.

Silicone oil has emerged as the common option for intraocular tamponade during complicated retina vitrectomy. However, the postoperative elevation of intraocular pressure (IOP), influenced by numerous factors, remains a significant and frequently encountered complication that poses a potential threat to vision. Extensive research has been conducted to investigate the risk factors associated with elevated IOP following silicone oil tamponade, including silicone oil viscosity, preoperative high IOP, diabetes, and lens status. This comprehensive review aims to gather and summarize the current research findings regarding the risk factors contributing to IOP elevation following silicone oil tamponade, as well as the optimal management strategies for secondary glaucoma. The analysis includes the physicochemical properties of silicone oil, preoperative and intraoperative risk factors, and the effective management of secondary glaucoma. Enhancing our understanding of the primary factors associated with silicone oil-induced IOP elevation will facilitate the guidance of timely and appropriate interventions.

Prediction model for elevated intraocular pressure risk after silicone oil filling based on clinical features.

Background: To evaluate risk factors and further develop prediction models for intraocular pressure elevation (IOP) after vitreoretinal surgery with silicone oil tamponade to support clinical management. Methods: A retrospective study analyzed 1,061 eyes of 1,061 consecutive patients that presented to the Jiangsu Province Hospital between December 2015 and December 2020, the IOP was measured from the preoperative visit and at the 1-week, 1-month, 3-month, and 6-month visits, and the final postoperative visit before silicone oil removal. Four machine learning methods were used to carried out the prediction of IOP elevation: Decision Tree, Logistic Regression, Random Forest, and Gradient-Boosted Decision Trees (GBDT) based on features including demographic and clinical characteristics, preoperative factors and surgical factors. Predictors were selected based on the p-value of the univariate analysis. Results: Elevated intraocular pressure developed in 26.01% of the eyes postoperatively. Elevated intraocular pressure primarily occurred within 1-2 weeks after surgery. Additionally, the majority of IOP values were distributed around 25-40 mmHg. GBDT utilizing features with p-values less than 0.5 from the hypothesis testing demonstrated the best predictive performance for 0.7944 in accuracy. The analysis revealed that age, sex, hypertension, diabetes, myopia, retinal detachment, lens status and biological parameters have predictive value. Conclusion: Age, sex, hypertension, diabetes, myopia, retinal detachment, lens status and biological parameters have influence on postoperative intraocular pressure elevation for patients with silicone oil tamponade after pars plana vitrectomy. The prediction model showed promising accuracy for the occurrence of IOP elevation. This may have some reference significance for reducing the incidence of high intraocular pressure after pars plana vitrectomy combined with silicone oil filling.