Fundus Diseases Research Articles

Transformer-Enhanced Retinal Vessel Segmentation for Diabetic Retinopathy Detection Using Attention Mechanisms and Multi-Scale Fusion

Eye health has become a significant concern in recent years, given the rising prevalence of visual impairment resulting from various eye disorders and related factors. Global surveys suggest that approximately 2.2 billion individuals are visually impaired, with at least 1 billion affected by treatable diseases or ailments. Early detection, treatment, and screening for fundus diseases are crucial in addressing these challenges. In this study, we propose a novel segmentation model for retinal vascular delineation aimed at diagnosing diabetic retinopathy. The model integrates CBAM (Channel-Attention and Spatial-Attention) for enhanced feature representation, JPU (Joint Pyramid Upsampling) for multi-scale feature fusion, and transformer blocks for contextual understanding. Leveraging deep-learning techniques, our proposed model outperforms existing approaches in retinal vascular segmentation, like achieving a Mean IOU of 0.8047, Recall of 0.7254, Precision of 0.8492, F1 Score of 0.7824, and Specificity of 0.9892 for CHASEDB1 dataset. Extensive evaluations on benchmark datasets demonstrate its efficacy, highlighting its potential for automated diabetic retinopathy screening.

Reactive Oxygen Species Responsive Supramolecular Prodrug Eyedrops for the Treatment of Choroidal Neovascularization.

Choroidal neovascularization (CNV) represents a hallmark of neovascular fundus diseases, including age-related macular degeneration and diabetic retinopathy. Traditional eyedrops have encountered formidable challenges in treating CNV, primarily due to their extremely poor intraocular bioavailability and potential adverse off-target effects. Herein, an ocular-permeable supramolecular prodrug eyedrop (Di-DAS/P-PCD) has been developed for the on-demand delivery of antiangiogenic agents in the oxidative microenvironment of CNV. The eyedrop nanoformulation is composed of cell-penetrating peptide-modified PEGylated cyclodextrin (P-PCD) and reactive oxygen species (ROS)-sensitive antiangiogenic dasatinib prodrug Di-DAS. In a laser-induced CNV mouse model, daily instillation of Di-DAS/P-PCD has achieved remarkable penetration into the choroid and significantly suppressed CNV growth while exhibiting a good biocompatibility profile. Our results highlight the potential of the supramolecular prodrug eyedrops as a versatile approach for the targeted treatment of CNV and other neovascular eye disorders.

A multi-class fundus disease classification system based on an adaptive scale discriminator and hybrid loss

Fundus images are crucial in the observation and detection of ophthalmic diseases. However, detecting multiple ophthalmic diseases from fundus images using deep learning techniques is a complex and challenging task One challenge is the complexity of fundus disease structures, which leads to low detection accuracy. Another challenge is the class imbalance problem common in multi-label image classification, which increases the difficulty of algorithm training and evaluation. To address these issues, this study leverages deep learning to propose an ophthalmic disease classification system. We first employ ResNet50 as the backbone network to extract image features, and then use our designed multi-dimensional attention module and adaptive scale discriminator to enhance the network's ability to detect disease features. During training, we innovatively propose a hybrid loss function method to improve the detection capability on imbalanced data. Finally, we conducted experiments on the ODRI-5K dataset with the proposed classification system. In the test set, our method achieved an AUC of 98.53 and an F1-score of 89.73. This result fully demonstrates the excellent disease classification capability of our method. In summary, the multi-label fundus image disease classification system we proposed exhibits outstanding recognition capability, providing an effective solution for the diagnosis of multi-label fundus image diseases.

A fundus vessel segmentation method based on double skip connections combined with deep supervision.

Fundus vessel segmentation is vital for diagnosing ophthalmic diseases like central serous chorioretinopathy (CSC), diabetic retinopathy, and glaucoma. Accurate segmentation provides crucial vessel morphology details, aiding the early detection and intervention of ophthalmic diseases. However, current algorithms struggle with fine vessel segmentation and maintaining sensitivity in complex regions. Challenges also stem from imaging variability and poor generalization across multimodal datasets, highlighting the need for more advanced algorithms in clinical practice. This paper aims to explore a new vessel segmentation method to alleviate the above problems. We propose a fundus vessel segmentation model based on a combination of double skip connections, deep supervision, and TransUNet, namely DS2TUNet. Initially, the original fundus images are improved through grayscale conversion, normalization, histogram equalization, gamma correction, and other preprocessing techniques. Subsequently, by utilizing the U-Net architecture, the preprocessed fundus images are segmented to obtain the final vessel information. Specifically, the encoder firstly incorporates the ResNetV1 downsampling, dilated convolution downsampling, and Transformer to capture both local and global features, which upgrades its vessel feature extraction ability. Then, the decoder introduces the double skip connections to facilitate upsampling and refine segmentation outcomes. Finally, the deep supervision module introduces multiple upsampling vessel features from the decoder into the loss function, so that the model can learn vessel feature representations more effectively and alleviate gradient vanishing during the training phase. Extensive experiments on publicly available multimodal fundus datasets such as DRIVE, CHASE_DB1, and ROSE-1 demonstrate that the DS2TUNet model attains F1-scores of 0.8195, 0.8362, and 0.8425, with Accuracy of 0.9664, 0.9741, and 0.9557, Sensitivity of 0.8071, 0.8101, and 0.8586, and Specificity of 0.9823, 0.9869, and 0.9713, respectively. Additionally, the model also exhibits excellent test performance on the clinical fundus dataset CSC, with F1-score of 0.7757, Accuracy of 0.9688, Sensitivity of 0.8141, and Specificity of 0.9801 based on the weight trained on the CHASE_DB1 dataset. These results comprehensively validate that the proposed method obtains good performance in fundus vessel segmentation, thereby aiding clinicians in the further diagnosis and treatment of fundus diseases in terms of effectiveness and feasibility.

Clinical macular edema following uneventful cataract surgery in eyes without pre-existing fundus diseases: risk factors and treatment prognosis

BackgroundTo investigate the risk factors and prognosis of clinical pseudophakic cystoid macular edema (PCME) after uneventful phacoemulsification surgery in patients without pre-existing fundus diseases.MethodsThis was a retrospective case-control study. Medical records between August 2020 and August 2023 were reviewed for patients who had no previous fundus diseases and developed clinical PCME. A control group was randomly chosen and the risk factors for PCME was analyzed by binary logistic regression. Structure and visual prognosis of the PCME cohort were observed and compared among subgroups undergoing different treatment measures.ResultsForty-seven eyes of 47 patients with PCME were included. The development of PCME was associated with higher systolic blood pressure (OR, 1.048; 95%CI 1.002, 1.097; P = .042), no posterior vitreous detachment (OR, 0.215; 95%CI: 0.553, 0.887; P = .032) and shorter axial lengths (OR, 0.401; 95%CI 0.161, 0.997; P = .049) compared to controls. During a mean follow-up of 8.26 months, 36 eyes (76.6%) showed visual improvement with decreased macular thickness. Different treatment modalities, including observation, topical NSAIDs, and intervention therapy, have no significant differences on the visual prognosis (P = 1.000). However, the intervention group had a shorter recovery time compared to the observation group (28.6 vs. 45.9 days, P = .037).ConclusionPCME remains an encountered morbidity in patients without pre-existing fundus diseases. Shorter axial lengths, absence of posterior vitreous detachment, and higher systolic blood pressure are risk factors of PCME. Active intervention failed to improve the prognosis of PCME but could shorten the recovery time.

Detection of artificial spots in fundus images using modified U-Net based semantic segmentation

Today, deep learning algorithms are playing a very crucial role in the initial stage diagnosis of several fundus diseases like glaucoma, hypertension, and diabetic retinopathy. Lots of research is going on in this area now-a-days. During the acquisition of fundus images some artificial spots (e.g. because of the device itself, dust particles in the surroundings) have been added in captured images. In this paper, artificial spots in the fundus images that are generated due to the non-standardized conditions of scanning devices, are detected with the help of a newly proposed modified UNet (mU-Net) semantic segmentation model. Initially, preprocessing methods such as Gaussian blur, thresholding, and Hough transform have been used to create artificial spots. Now these preprocessed images have been used for training the proposed model. To make the proposed model more effective, the following modifications like, Regularization techniques (early stopping, greater weight decay, and Adam optimizer), decay learning rate scheduler, categorical cross-entropy loss function, and a significant number of filters have been modified in simple U-Net model. Apart from these mentioned modifications in the base U-Net model, a feature injecting module (FIM) has been added between the expansion and the contraction section of the simple U-Net model. FIM adds the features of the input image at the time of up-sampling. The addition of FIM to a simple U-Net model improves the detection of artificial spots and enhances the performance of the model. The mU-Net has been compared with other models, namely simple U-Net, V-Net, UNet++, ResUnet-a, WideU-Net, and Swin-Unet. The Friedman test that has been conducted on IOU, DICE, MAE, PSNR, and SSIM scores, found that mU-Net balances evaluation metrics well. It appears that the nonparametric Friedman test will improve reproducibility by demonstrating statistical significance. The IOU, DICE, MAE, PSNR, and SSIM scores of the proposed model indicate superior performance as compared to other models.

OphGLM: An ophthalmology large language-and-vision assistant

Vision computer-aided diagnostic methods have been used in early ophthalmic disease screening and diagnosis. However, the limited output formats of these methods lead to poor human–computer interaction and low clinical applicability value. Thus, ophthalmic visual question answering is worth studying. Unfortunately, no practical solutions exist before Large Language Models(LLMs). In this paper, we investigate the ophthalmic visual diagnostic interaction problem. We construct an ophthalmology large language-and-vision assistant, OphGLM, consisting of an image encoder, a text encoder, a fusion module, and an LLM module. We establish a new Chinese ophthalmic fine-tuning dataset, FundusTuning-CN, including the fundus instruction and conversation sets. Based on FundusTuning-CN, we establish a novel LLM-tuning strategy to introduce visual model understanding and ophthalmic knowledge into LLMs at a low cost and high efficiency. Leveraging the pre-training of the image encoder, OphGLM demonstrates strong visual understanding and surpasses open-source visual language models in common fundus disease classification tasks. The FundusTuning-CN enables OphGLM to surpass open-source medical LLMs in both ophthalmic knowledge and interactive capabilities. Our proposed OphGLM has the potential to revolutionize clinical applications in ophthalmology. The dataset, code, and models will be publicly available at https://github.com/ML-AILab/OphGLM.

A Meta-Learning Approach for Classifying Multimodal Retinal Images of Retinal Vein Occlusion With Limited Data.

To propose and validate a meta-learning approach for detecting retinal vein occlusion (RVO) from multimodal images with only a few samples. In this cross-sectional study, we formulate the problem as meta-learning. The meta-training dataset consists of 1254 color fundus (CF) images from 39 different fundus diseases. Two meta-testing datasets include a public domain dataset and an independent dataset from Kandze Prefecture People's Hospital. The proposed meta-learning models comprise two modules: the feature extraction networks and the prototypical networks (PNs). We use two deep learning models (the ResNet and the Contrastive Language-Image Pre-Training networks [CLIP]) for feature extraction. We evaluate the performance of the algorithms using accuracy, area under the receiver operating characteristic curve (AUCROC), F1-score, and recall. CLIP-based PNs outperform across all meta-testing datasets. For the public APTOS dataset, meta-learning algorithms achieve good results with an accuracy of 86.06% and an AUCROC of 0.87 with only 16 training images. In the hospital datasets, meta-learning algorithms show excellent diagnostic capability for detecting RVO with a very low number of shots (AUCROC above 0.99 for n = 4, 8, and 16, respectively). Notably, even though the meta-training dataset does not include fluorescein angiography (FA) images, meta-learning algorithms also have excellent diagnostic capability for detecting RVO from images with a different modality (AUCROC above 0.93 for n = 4, 8, and 16, respectively). The proposed meta-learning models excel in detecting RVO, not only on CF images but also on FA images from a different imaging modality. The proposed meta-learning models could be useful in automatically detecting RVO on CF and FA images.

Evaluation of AI-enhanced non-mydriatic fundus photography for diabetic retinopathy screening

ObjectiveTo assess the feasibility of using non-mydriatic fundus photography in conjunction with an artificial intelligence (AI) reading platform for large-scale screening of diabetic retinopathy (DR). MethodsIn this study, we selected 120 patients with diabetes hospitalized in our institution from December 2019 to April 2021. Retinal imaging of 240 eyes was obtained using non-mydriatic fundus photography. The fundus images of these patients were divided into two groups based on different interpretation methods. In Experiment Group 1, the images were analyzed and graded for DR diagnosis using an AI reading platform. In Experiment Group 2, the images were analyzed and graded for DR diagnosis by an associate chief physician in ophthalmology, specializing in fundus diseases. Concurrently, all patients underwent the gold standard for DR diagnosis and grading—fundus fluorescein angiography (FFA)—with the outcomes serving as the Control Group. The diagnostic value of the two methods was assessed by comparing the results of Experiment Groups 1 and 2 with those of the Control Group. ResultsKeeping the control group (FFA results) as the gold standard, no significant differences were observed between the two experimental groups regarding diagnostic sensitivity, specificity, false positive rate, false negative rate, positive predictive value, negative predictive value, Youden's index, Kappa value, and diagnostic accuracy (X2 = 0.371, P > 0.05). ConclusionCompared with the manual reading group, the AI reading group revealed no significant differences across all diagnostic indicators, exhibiting high sensitivity and specificity, as well as a relatively high positive predictive value. Additionally, it demonstrated a high level of diagnostic consistency with the gold standard. This technology holds potential for suitability in large-scale screening of DR.

How Advanced are Nanocarriers for Effective Subretinal Injection?

Subretinal injection (SR injection) is a commonly used method of ocular drug delivery and has been mainly applied for the treatment of neovascular age-associated macular degeneration (nAMD) and sub-macular hemorrhage (SMH) caused by nAMD, as well as various types of hereditary retinopathies (IRD) such as Stargardt's disease (STGD), retinitis pigmentosa (RP), and a series of fundus diseases such as Leber's congenital dark haze (LCA), choroidal defects, etc. The commonly used carriers of SR injection are mainly divided into viral and non-viral vectors. Leber's congenital amaurosis (LCA), choroidal agenesis, and a series of other fundus diseases are also commonly treated using SR injection. The commonly used vectors for SR injection are divided into two categories: viral vectors and non-viral vectors. Viral vectors are a traditional class of SR injection drug carriers that have been extensively studied in clinical treatment, but they still have many limitations that cannot be ignored, such as poor reproduction efficiency, small loading genes, and triggering of immune reactions. With the rapid development of nanotechnology in the treatment of ocular diseases, nanovectors have become a research hotspot in the field of non-viral vectors. Nanocarriers have numerous attractive properties such as low immunogenicity, robust loading capacity, stable structure, and easy modification. These valuable features imply greater safety, improved therapeutic efficacy, longer duration, and more flexible indications. In recent years, there has been a growing interest in nanocarriers, which has led to significant advancements in the treatment of ocular diseases. Nanocarriers have not only successfully addressed clinical problems that viral vectors have failed to overcome but have also introduced new therapeutic possibilities for certain classical disease types. Nanocarriers offer undeniable advantages over viral vectors. This review discusses the advantages of subretinal (SR) injection, the current status of research, and the research hotspots of gene therapy with viral vectors. It focuses on the latest progress of nanocarriers in SR injection and enumerates the limitations and future perspectives of nanocarriers in the treatment of fundus lesions. Furthermore, this review also covers the research progress of nanocarriers in the field of subretinal injection and highlights the value of nanocarrier-mediated SR injection in the treatment of fundus disorders. Overall, it provides a theoretical basis for the application of nanocarriers in SR injection.

Exploring and identifying the imaging biomarkers for predicting anti-VEGF treatment response in polypoidal choroidal vasculopathy: aprospective multicenter study.

Polypoidal choroidal vasculopathy (PCV) is a hemorrhagic fundus disease that can lead to permanent vision loss. Predicting the treatment response to anti-VEGF monotherapy in PCV is consistently challenging. We aimed to conduct a prospective multicenter study to explore and identify the imaging biomarkers for predicting the anti-VEGF treatment response in PCV patients, establish predictive model, and undergo multicenter validation. This prospective multicenter study utilized clinical characteristics and images of treatment naïve PCV patients from 15 ophthalmic centers nationwide to screen biomarkers, develop model, and validate its performance. Patients from Peking Union Medical College Hospital were randomly divided into a training set and an internal validation set. A nomogram was established by univariate, LASSO regression, and multivariate regression analysis. Patients from the other 14 centers served as an external test set. Area under the curve (AUC), sensitivity, specificity, and accuracy were calculated. Decision curve analysis (DCA) and clinical impact curve (CIC) were utilized to evaluate the practical utility in clinical decision-making. The eye distribution for the training set, internal validation set, and external test set were 66, 31, and 71, respectively. The 'Good responder' exhibited a thinner subfoveal choroidal thickness (SFCT) (230.67 ± 61.96 vs. 314.42 ± 88.00 μm, p < 0.001), lower choroidal vascularity index (CVI) (0.31 ± 0.08 vs. 0.36 ± 0.05, p = 0.006), fewer choroidal vascular hyperpermeability (CVH) (31.0 vs. 62.2%, p = 0.012), and more intraretinal fluid (IRF) (58.6 vs. 29.7%, p = 0.018). SFCT (OR 0.990; 95% CI 0.981-0.999; p = 0.033) and CVI (OR 0.844; 95% CI 0.732-0.971; p = 0.018) were ultimately included as the optimal predictive biomarkers and presented in the form of a nomogram. The model demonstrated AUC of 0.837 (95% CI 0.738-0.936), 0.891 (95% CI 0.765-1.000), and 0.901 (95% CI 0.824-0.978) for predicting 'Good responder' in the training set, internal validation set, and external test set, respectively, with excellent sensitivity, specificity, and practical utility. Thinner SFCT and lower CVI can serve as imaging biomarkers for predicting good treatment response to anti-VEGF monotherapy in PCV patients. The nomogram based on these biomarkers exhibited satisfactory performances.

Cnn-trans model: A parallel dual-branch network for fundus image classification

The existence of fundus diseases not only endangers people’s vision, but also brings serious economic burden to the society. Fundus images are an objective and standard basis for the diagnosis of fundus diseases. With the continuous advancement of computer science, deep learning methods dominated by convolutional neural networks (CNN) have been widely used in fundus image classification. However, the current CNN-based fundus image classification research still has a lot of room for improvement: CNN cannot effectively avoid the interference of repeated background information and has limited ability to model the whole world. In response to the above findings, this paper proposes the CNN-Trans model. The CNN-Trans model is a parallel dual-branch network, which is the two branches of CNN-LSTM and Vision Transform (ViT). The CNN-LSTM branch uses Xception after transfer learning. As the original feature extractor, LSTM is responsible for dealing with the gradient disappearance problem in neural network iterations before the classification head, and then introduces a new type of lightweight attention mechanism between Xception and LSTM: Coordinate Attention, so as to emphasize the key information related to classification and suppress the less useful repeated background information; while the self-attention mechanism in the ViT branch is not limited by local interactions, it can establish long-distance dependence on the target and extract global features. Finally, the concatenation (Concat) operation is used to fuse the features of the two branches. The local features extracted by the CNN-LSTM branch and the global features extracted by the ViT branch form complementary advantages. After feature fusion, more comprehensive image feature information is sent to the to the classification layer. Finally, after a large number of experimental tests and comparisons, the results show that: the CNN-Trans model achieved an accuracy of 80.68% on the fundus image classification task, and the CNN-Trans model has a classification that is comparable to the state-of-the-art methods. performance..

The innovation and challenge of artificial intelligence in the whole process management of fundus disease

Artificial intelligence (AI) has demonstrated revolutionary potential and wide-ranging applications in the comprehensive management of fundus diseases, yet it faces challenges in clinical translation, data quality, algorithm interpretability, and cross-cultural adaptability. AI has proven effective in the efficient screening, accurate diagnosis, personalized treatment recommendations, and prognosis prediction for conditions such as diabetic retinopathy, age-related macular degeneration, and other fundus diseases. However, there is a significant gap between the need for large-scale, high-quality, and diverse datasets and the limitations of current research data. Additionally, the black-box nature of AI algorithms, the acceptance by clinicians and patients, and the generalizability of these algorithms pose barriers to their widespread clinical adoption. Researchers are addressing these challenges through approaches such as federated learning, standardized data collection, and prospective trials to enhance the robustness, interpretability, and practicality of AI systems. Despite these obstacles, the benefits of AI in fundus disease management are substantial. These include improved screening efficiency, support for personalized treatment, the discovery of novel disease characteristics, and the development of precise treatment strategies. Moreover, AI facilitates the advancement of telemedicine through 5G and the Internet of Things. Future research should continue to tackle existing issues, fully leverage the potential of AI in the prevention and treatment of fundus diseases, and advance intelligent, precise, and remote ophthalmic services to meet global eye health needs.

Advancements in Ophthalmic Healthcare with Deep Learning-Driven Segmentation for Multi-Stage Eye Fundus Disease Diagnosis

The global rise in eye diseases highlights the need for advanced diagnostic tools in ophthalmic care. This project introduces a deep learning model for classifying eye diseases, streamlining diagnosis, and improving accuracy. Using real-time images from reputable healthcare facilities like Bajwa Hospital in Punjab and Shang gong Medical Tech in China, the model is fine-tuned to clinical nuances. Segmentation of the optic disc and blood vessels is key for precise retinal structure delineation, enhancing disease identification. Various CNN models, including Mobile Net, Dense Net, Reset, and a custom CNN, were utilized for retinal image analysis. Additionally, the Vision Transformer (ViT) model was integrated to capture intricate patterns. The model is deployed as a web application using Django, HTML, SQLite, and Bootstrap, featuring a secure, user-friendly interface. Users can input images to receive prompt disease predictions, along with verified information on prevention, treatment options, and medications. This system not only automates and improves diagnostic processes but also provides reliable medical guidance.

Improve the efficiency and accuracy of ophthalmologists’ clinical decision-making based on AI technology

BackgroundAs global aging intensifies, the prevalence of ocular fundus diseases continues to rise. In China, the tense doctor-patient ratio poses numerous challenges for the early diagnosis and treatment of ocular fundus diseases. To reduce the high risk of missed or misdiagnosed cases, avoid irreversible visual impairment for patients, and ensure good visual prognosis for patients with ocular fundus diseases, it is particularly important to enhance the growth and diagnostic capabilities of junior doctors. This study aims to leverage the value of electronic medical record data to developing a diagnostic intelligent decision support platform. This platform aims to assist junior doctors in diagnosing ocular fundus diseases quickly and accurately, expedite their professional growth, and prevent delays in patient treatment. An empirical evaluation will assess the platform’s effectiveness in enhancing doctors’ diagnostic efficiency and accuracy.MethodsIn this study, eight Chinese Named Entity Recognition (NER) models were compared, and the SoftLexicon-Glove-Word2vec model, achieving a high F1 score of 93.02%, was selected as the optimal recognition tool. This model was then used to extract key information from electronic medical records (EMRs) and generate feature variables based on diagnostic rule templates. Subsequently, an XGBoost algorithm was employed to construct an intelligent decision support platform for diagnosing ocular fundus diseases. The effectiveness of the platform in improving diagnostic efficiency and accuracy was evaluated through a controlled experiment comparing experienced and junior doctors.ResultsThe use of the diagnostic intelligent decision support platform resulted in significant improvements in both diagnostic efficiency and accuracy for both experienced and junior doctors (P < 0.05). Notably, the gap in diagnostic speed and precision between junior doctors and experienced doctors narrowed considerably when the platform was used. Although the platform also provided some benefits to experienced doctors, the improvement was less pronounced compared to junior doctors.ConclusionThe diagnostic intelligent decision support platform established in this study, based on the XGBoost algorithm and NER, effectively enhances the diagnostic efficiency and accuracy of junior doctors in ocular fundus diseases. This has significant implications for optimizing clinical diagnosis and treatment.

Automatic diagnosis of pediatric high myopia via Attention-based Patch Residual Shrinkage network

Fundus images can be obtained non-invasively and be adopted to monitor the follow-up on various fundus diseases, such as high myopia. Therefore, the use of fundus images for the early screening of eye diseases has principal clinical significance. However, AI-based medical research continues to face two main challenges, lack of prior-knowledge guidance and complex fundus information. In this paper, we propose a pediatric high myopia classification model, the Attention-based Patch Residual Shrinkage network (APRSnet), which facilitates early clinical diagnosis by evaluating the importance of different image features. A collection of 2492 high-resolution fundus images of children, including 768 images of high myopia and 1724 images of non-high myopia, is fed to APRSnet as the training dataset. To better understand how different features impact the classification, APRSnet is tested on multiple feature-enhanced fusion datasets. The result shows that, by removing irrelevant information in fundus images, APRSnet achieves an accuracy of 0.959 and an F1 score of 0.946 and outperforms all classic image classification networks we compared with. We also testify that, in fundus images, gradient information benefits severity classification the most since it helps with model convergence more than luminance and texture information.

Trends and hot spots in research related to aqueous humor from 2014 to 2023: A bibliometric analysis

PurposeTo analyze publication trends and investigate research hotspots of aqueous humor (AH) studies. MethodsA bibliometric study was conducted based on the Web of Science Core Collection (WOSCC). VOSviewer v. 1.6.18 was utilized to create a knowledge map visualizing the number of annual publications, the distribution of countries, international collaborations, author productivity, source journals and keywords in the field. ResultsA grand total of 4020 peer-reviewed papers concerning AH were retrieved from 2014 to 2023. The United States of America secured the top position among the most published countries and Duke University emerged as the most active institution. Stamer, WD contributed the most papers in this area. Investigative Ophthalmology & Visual Science was the most prolific journal in AH research. Retrieved publications mainly concentrated on the correlation between AH as a biomarker carrier and different ocular disorders. Six clusters were formed based on the keywords: (1) the diagnosis of endophthalmitis and AH pharmacokinetics; (2) the association of AH with pathogenesis and prognosis of glaucoma; (3) diagnosis and treatment of AH associated with uveitis; (4) the relationship between AH and refractive diseases of the eye; (5) the association of AH with mechanism and biomarkers of ocular tumorigenesis; (6) the indicators of AH associated with fundus disease. ConclusionsThis study unveiled present patterns of global collaboration, emerging frontiers, fundamental knowledge, research hotspots and current trends in AH.

Ophthalmic Tethered Gold Yarnball‐Mediated Retained Drug Delivery for Eye Fundus Disease Treatment

Ophthalmic Tethered Gold Yarnball‐Mediated Retained Drug Delivery for Eye Fundus Disease Treatment

The Role and Therapeutic Potential of Melatonin in Degenerative Fundus Diseases: Diabetes Retinopathy and Age-Related Macular Degeneration.

Degenerative fundus disease encompasses a spectrum of ocular diseases, including diabetic retinopathy (DR) and age-related macular degeneration (AMD), which are major contributors to visual impairment and blindness worldwide. The development and implementation of effective strategies for managing and preventing the onset and progression of these diseases are crucial for preserving patients' visual acuity. Melatonin, a neurohormone primarily produced by the pineal gland, exhibits properties such as circadian rhythm modulation, antioxidant activity, anti-inflammatory effects, and neuroprotection within the ocular environment. Furthermore, melatonin has been shown to suppress neovascularization and reduce vascular leakage, both of which are critical in the pathogenesis of degenerative fundus lesions. Consequently, melatonin emerges as a promising therapeutic candidate for degenerative ocular diseases. This review provides a comprehensive overview of melatonin synthesis, its localization within ocular tissues, and its mechanisms of action, particularly in regulating melatonin production, thereby underscoring its potential as a therapeutic agent for degenerative fundus diseases.

Novel fabrication of anti-VEGF drug ranibizumab loaded PLGA/PLA co-polymeric nanomicelles for long-acting intraocular delivery in the treatment of age-related macular degeneration therapy

Age associated macular degeneration is the 3rd primary cause of blind fundus diseases globally. A reliable and long-lasting method of intraocular drug delivery is still needed. Herein, this study was aim to develop the novel fabrication of ranibizumab loaded co-polymeric nanomicelles (Rabz-CP-NMs) for AMD. The CMC of co-polymeric nanomicelles was determined to be low, at 6.2μg/ml. The ring copolymerization method was employed to fabricate the NMs and characterize via FTIR, XRD, TEM, DLS and Zeta potential. Rabz-CP-NMs was spherical shape with 10-50nm in size. Stable and prolonged drug release was achieved with the Rabz from CP-NMs at 48h. D407 and ARPE19 ocular cell lines showed dose-dependent cell viability with Rabz-CP-NMs. The Rabz-CP-NMs also had less toxicity, higher uptake, lower cell death and prolonged VEGF-A inhibition, as shown by cytoviability assay. Thus, Rabz-CP-NMs were safe for ocular use, suggesting that could be used to improve intraocular AMD treatment.