Background: To analyze a case of atypical bilateral papilledema in a puerperium woman, and to explore the pathogenic mechanism of pregnancy-related physiological changes, blood hypercoagulable state, immune abnormalities and abnormal structure of intracranial venous sinus, so as to provide reference for early diagnosis and intervention of similar cases.Case report: A 28-year-old woman, 3 days post-operative from a cesarean section, presented at the hospital with decreased vision in her right eye. An examination revealed bilateral papilledema. She did not have typical symptoms like dizziness, headache, or pulsatile tinnitus. During pregnancy, she had taken hydroxychloroquine orally for 5 months due to elevated immune indexes. She also received anticoagulant therapy for lower extremity venous thrombosis a month prior and had a history of cerebrospinal fluid leakage repair for intracranial hypotension syndrome a year ago. Fundus photography and OCT showed bilateral papilledema and macular edema in the right eye, with slightly enlarged physiological blind spots in both eyes. Her pre-pregnancy BMI was 16.5, and postpartum BMI was 22. Laboratory tests indicated a D-dimer level exceeding 20 mg/L and abnormal immune indicators. Ophthalmic color Doppler ultrasound demonstrated bilateral optic nerve sheath widening, with measurements of 0.625 cm on the right and 0.590 cm on the left, suggesting potential elevated intracranial pressure. MRV detected stenosis in the right distal sigmoid sinus and proximal transverse sinus, while the left sigmoid sinus and transverse sinus were not visualized. The patient was diagnosed with increased intracranial pressure caused by multiple factors. Treatment with mannitol to reduce intracranial pressure, along with anticoagulation and other supportive and symptomatic treatments, was administered. After 1 week, macular edema in the right eye subsided, vision improved, and bilateral papilledema slowly improved.Conclusion: This case provides multi-dimensional clinical evidence for the differential diagnosis of puerperium papilledema. For patients with low BMI and atypical symptoms of bilateral papilledema during puerperium, it is necessary to be alert to multiple pathogenic factors. It is recommended to preferentially screen intracranial venous sinus lesions and detect immune indicators by imaging. Ocular ultrasound can be used as a non-invasive screening method for intracranial hypertension.

Many major diseases can cause changes in the morphology of blood vessels, and the segmentation of retinal blood vessels is of great significance for preventing these diseases. Obtaining complete, continuous, and high-resolution segmentation results is very challenging due to the diverse structures of retinal tissues, the complex spatial structures of blood vessels, and the presence of many small ships. In recent years, deep learning networks like UNet have been widely used in medical image processing. However, the continuous down-sampling operations in UNet can result in the loss of a significant amount of information. Although skip connections between the encoder and decoder can help address this issue, the encoder features still contain a large amount of irrelevant information that cannot be efficiently utilized by the decoder. To alleviate the irrelevant information, this paper proposes a feature selection module between the decoder and encoder that utilizes the self-attention mechanism of transformers to accurately and efficiently select the relevant encoder features for the decoder. Additionally, a lightweight Residual Global Context module is proposed to obtain dense global contextual information and establish dependencies between pixels, which can effectively preserve vascular details and segment small vessels accurately and continuously. Experimental results on three publicly available color fundus image datasets (DRIVE, CHASE, and STARE) demonstrate that the proposed algorithm outperforms existing methods in terms of both performance metrics and visual quality.

Background: To systematically evaluate the information quality, reliability, and content characteristics of short videos related to age-related macular degeneration on the Chinese mainland version of TikTok, filling the research gap in this field and providing references for ophthalmic health education and platform information governance. Methods: This cross-sectional study was conducted on October 15, 2025, by searching the keyword “年龄相关性黄斑变性” on TikTok. The top 200 videos under “comprehensive ranking” were screened, and 196 videos meeting the eligibility criteria were ultimately included. Content integrity was evaluated following the American Academy of Ophthalmology guidelines and the Goobie framework. Video quality was assessed using the DISCERN instrument and the PEMAT-A/V tool. Statistical analyses were performed in IBM SPSS Statistics 27.0, with inter-rater reliability measured by the intraclass correlation coefficient. Differences among groups and associations between variables were examined using ANOVA and correlation analysis. Results: The overall quality of the included videos was moderate, with a median DISCERN tool score of 48.00 and a median Overall quality score of 3.00. The Understandability score was relatively high (median 84.62%), whereas the Actionability score was lower (median 75.00%). Videos uploaded by the Non-Profit group showed the highest quality (mean DISCERN tool score 59.13 ± 2.50), followed by the Medical group. The Non-Medical and For-Profit groups demonstrated the lowest quality, with statistically significant differences among groups ( P < 0.05). Quality metrics were moderately positively correlated with user engagement metrics. The correlation coefficients between reliability and engagement were r = 0.48–0.54 ( P < 0.05). Video duration showed a mild positive correlation with both quality and engagement ( r = 0.23–0.30, P < 0.05). Inter-rater reliability was good (intraclass correlation coefficient = 0.839–0.947, P < 0.001). Conclusion: Age-related macular degeneration videos on TikTok showed moderate overall quality, with content emphasizing clinical concerns but neglecting basic knowledge. Information quality varied by uploader source, with non-profit organizations and medical professionals providing most high-quality content. Higher-quality videos tended to receive greater user engagement, suggesting that platform algorithms may preferentially spread better educational material. These findings provide empirical support for improving science communication on this disease and strengthening information quality management on digital platforms.

Background: The early diagnosis and treatment of eye diseases is crucial for preserving vision, and optical coherence tomography (OCT) has become a common non-invasive technique for eye examinations. In particular, retinal vessel segmentation in OCT images is a critical task that provides detailed information such as vessel diameter and wall thickness, serving as important diagnostic aids for ophthalmologists. This study aimed to develop an efficient and accurate retinal vessel segmentation method in OCT images to improve the diagnosis and treatment of eye diseases. Methods: Transformer Dual Segment Network (TransDualSegNet) involves several key steps: firstly, the OCT images are preprocessed through denoising and brightness adjustment to enhance the performance of subsequent segmentation algorithms. Secondly, we introduce an encoder-decoder model with the Pyramid Vision Transformer as the encoding layer, which automatically learns the feature representation of retinal vessels to improve segmentation accuracy and robustness. The decoder utilizes a dual-branch convolutional network that incorporates and emphasizes the edge information of retinal vessel OCT images as complementary information for vessel segmentation. Finally, a feature fusion module (FFM) is applied to fuse the global features and edge features, which are fed into the edge extraction module to generate the final edge probability map and obtain the segmentation results. Results: Experimental results on the OCT vessel segmentation dataset showed that the proposed model outperformed U-Net, Attention U-Net, and Trans U-Net, with accuracy (ACC) 91.13%±0.11%, sensitivity (SE) 90.02%±0.38%, Jaccard similarity (JS) 93.92%±0.13%, and Dice coefficient (DICE) 90.26%±0.25% all higher than comparisons. The model has 9.82 M parameters, falling between the comparison models, and a forward inference time of 20.3 ms, faster than Attention U-Net and Trans U-Net, balancing high accuracy and efficiency. Ablation experiments showed that it outperformed all sub-models in metrics such as ACC, SE, specificity (SP), Intersection over Union (IOU), and area under the curve (AUC), with IOU reaching 93.92%±0.13% and AUC 93.04%±0.34%, verifying the synergistic effectiveness of each component. Conclusions: TransDualSegNet, with its innovative architecture and outstanding segmentation performance, offers a novel solution for retinal vessel segmentation. Although there is room for optimization, its technical advantages and clinical potential provide valuable insights for advancing medical image processing and ophthalmic diagnostic technologies. Precise retinal vessel segmentation aids in diagnosing diabetic retinopathy and hypertensive retinopathy, facilitating automatic measurement of parameters such as vessel diameter and wall thickness to offer more accurate treatment recommendations for patients.

Corneal neovascularization (CNV) is one of the leading causes of corneal blindness, affecting millions of people worldwide. Anti-vascular endothelial growt

Diabetic retinopathy (DR) is the leading cause of vision loss among working-age adults. Early screening and precise staging are crucial for delaying disease progression. Although the traditional Early Treatment of Diabetic Retinopathy Study (ETDRS) 7-field method is the gold standard for grading, it covers only 35% of the retinal area, carrying a risk of missing peripheral lesions. Ultra-widefield color fundus photography (UWF-CFP) overcomes this limitation by capturing up to 200° of the retina in a single image, enabling comprehensive visualization of peripheral ischemia, neovascularization, and peripheral–posterior pole asymmetry lesions. In recent years, combining UWF-CFP with deep learning algorithms has achieved robust performance in automated DR screening, grading, and quantitative vascular analysis. When further integrated with ultra-widefield swept-source optical coherence tomography angiography (UWF SS-OCTA) or multimodal clinical data, these AI-driven methods can improve diagnostic consistency and staging accuracy. Moreover, because the retinal microvasculature mirrors systemic microcirculation, vascular parameters from UWF-CFP have shown correlations with diabetic nephropathy, coronary artery calcification, and stroke risk, highlighting a potential role in non-invasive assessment of systemic complication risks in diabetic patients. UWF-CFP is facilitating a shift in the DR assessment paradigm from ETDRS 7-field to full retinal assessment. Combined with artificial intelligence and multimodal data integration, UWF-CFP has the potential to contribute to a future care model that integrates ocular and systemic risk assessment, which we tentatively term “eye–system collaborative management,” though this paradigm remains to be validated in prospective studies.