Ophthalmic Diseases Research Articles

Social activity in older people with visual impairment

Introduction. A potential factor that significantly reduces the social activity of older people is visual impairment due to various diseases. Visual impairment caused by cataract and diabetic retinopathy changes the social activity of people with the above-mentioned pathology, but it is extremely rarely analyzed by specialists and researchers, which does not allow identifying the limitations of patients in the social sphere and providing targeted medical and social care.
 The purpose of the study is to study social activity in older patients with diabetic retinopathy and cataract. 
 Materials and methods. According to a special questionnaire, one hundred fifteen 60–74 years patients suffered from cataract and diabetic retinopathy and 60–74 years 102 people without ophthalmic diseases were surveyed based on the S.N. Fedorov National medical research center «MNTK Eye Microsurgery», Tambov.
 Results. The average score of social activity in patients with cataract and diabetic retinopathy is statistically was found to be significantly lower, amounting to 3.10 ± 0.08 points versus 5.38 ± 0.09 points in people without ophthalmopathology. The main reasons that reduce social activity in patients with visual impairment are problems with making various purchases and using public or private transport. Factor analysis confirmed the contribution of these causes to the decline in social activity to be the highest and amounts to 0.876 and 0.708, respectively.
 Limitations. The study was carried out just in one territory, which makes it difficult to extrapolate data to other regions.
 Conclusion. It is recommended to increase social assistance to patients with cataract and diabetic retinopathy in solving problems of using transport and buying various goods.
 Limitations of the study. The study was carried out in one territory, which makes it difficult to extrapolate data to other regions.

Extracellular vesicle biomarkers in ocular fluids associated with ophthalmic diseases

Extracellular vesicles (EVs) are released as highly stable lipid bilayer particles carrying proteins, lipids, glycans and miRNAs. The contents of EVs vary based on the cellular origin, biogenesis route and the functional state of the cell suggesting certain diseased conditions. A growing body of evidence show that EVs carry important molecules implicated in the development and progression of ophthalmic diseases. EVs associated with ophthalmic diseases are mainly carried by one of the three ocular biofluids which include tears, aqueous humor and vitreous humor. This review summarizes the list of EV derived biomarkers identified thus far in ocular fluids for ophthalmic disease diagnosis. Further, the methods used for sample collection, sample volume and the sample numbers used in these studies have been highlighted. Emphasis has been given to describe the EV isolation and the characterization methods used, EV size profiled and the EV concentrations analyzed by these studies, thus providing a roadmap for future EV biomarker studies in ocular fluids.

Explainable artificial intelligence in deep learning neural nets-based digital images analysis

This review shows the capabilities of artificial intelligence in the analysis of digital images in the field of medicine using convolutional neural networks of deep learning. A new generation of artificial intelligence systems is described with an explanation of decision-making algorithms to the user — explainable artificial intelligence (XAI). The taxonomy of the methods of explanation and the description of the methods themselves are given. The substantiation of the need to use explainable artificial intelligence in classification tasks is given on the example of ophthalmic diseases. The study of the components of deep learning methods used in the reviewed works (neural network architecture, accuracy, characteristics of data sets) and explainable artificial intelligence (methods of explanation, criteria for the accuracy of explanation). As an example, the problem of recognizing two of the most commonly diagnosed eye diseases: diabetic retinopathy and glaucoma by artificial neural networks is considered.

SeqCorr-EUNet: A sequence correction dual-flow network for segmentation and quantification of anterior segment OCT image

The accurate segmentation of AS-OCT images is a prerequisite for the morphological details analysis of anterior segment structure and the extraction of clinical biological parameters, which play an essential role in the diagnosis, evaluation, and preoperative prognosis management of many ophthalmic diseases. Manually marking the boundaries of the anterior segment tissue is time-consuming and error-prone, with inherent speckle noise, various artifacts, and some low-quality scanned images further increasing the difficulty of the segmentation task. In this work, we propose a novel model called SeqCorr-EUNet with a dual-flow architecture based on convolutional gated recursive sequence correction for semantic segmentation and quantification of AS-OCT images. An EfficientNet encoder is employed to enhance the intra-slice features extraction ability of semantic segmentation flow. The sequence correction flow based on ConvGRU is introduced to extract inter-slice features from consecutive adjacent slices. Spatio-temporal information is fused to correct the morphological details of pre-segmentation results. And the channel attention gate is inserted into the skip-connection between encoder and decoder to enrich the contextual information and suppress the noise of irrelevant regions. Based on the segmentation results of the anterior segment structures, we achieved automatic extraction of essential clinical parameters, 3D reconstruction of the anterior chamber structure, and measurement of anterior chamber volume. The proposed SeqCorr-EUNet has been evaluated on the public AS-OCT dataset. The experimental results show that our method is competitive compared with the existing methods and significantly improves the segmentation and quantification performance of low-quality imaging structures in AS-OCT images.

Self-attention CNN for retinal layer segmentation in OCT.

The structure of the retinal layers provides valuable diagnostic information for many ophthalmic diseases. Optical coherence tomography (OCT) obtains cross-sectional images of the retina, which reveals information about the retinal layers. The U-net based approaches are prominent in retinal layering methods, which are usually beneficial to local characteristics but not good at obtaining long-distance dependence for contextual information. Furthermore, the morphology of retinal layers with the disease is more complex, which brings more significant challenges to the task of retinal layer segmentation. We propose a U-shaped network combining an encoder-decoder architecture and self-attention mechanisms. In response to the characteristics of retinal OCT cross-sectional images, a self-attentive module in the vertical direction is added to the bottom of the U-shaped network, and an attention mechanism is also added in skip connection and up-sampling to enhance essential features. In this method, the transformer's self-attentive mechanism obtains the global field of perception, thus providing the missing context information for convolutions, and the convolutional neural network also efficiently extracts local features, compensating the local details the transformer ignores. The experiment results showed that our method is accurate and better than other methods for segmentation of the retinal layers, with the average Dice scores of 0.871 and 0.820, respectively, on two public retinal OCT image datasets. To perform the layer segmentation of retinal OCT image better, the proposed method incorporates the transformer's self-attention mechanism in a U-shaped network, which is helpful for ophthalmic disease diagnosis.

LA-Net: layer attention network for 3D-to-2D retinal vessel segmentation in OCTA images

Objective. Retinal vessel segmentation from optical coherence tomography angiography (OCTA) volumes is significant in analyzing blood supply structures and the diagnosing ophthalmic diseases. However, accurate retinal vessel segmentation in 3D OCTA remains challenging due to the interference of choroidal blood flow signals and the variations in retinal vessel structure. Approach. This paper proposes a layer attention network (LA-Net) for 3D-to-2D retinal vessel segmentation. The network comprises a 3D projection path and a 2D segmentation path. The key component in the 3D path is the proposed multi-scale layer attention module, which effectively learns the layer features of OCT and OCTA to attend to the retinal vessel layer while suppressing the choroidal vessel layer. This module also efficiently captures 3D multi-scale information for improved semantic understanding during projection. In the 2D path, a reverse boundary attention module is introduced to explore and preserve boundary and shape features of retinal vessels by focusing on non-salient regions in deep features. Main results. Experimental results in two subsets of the OCTA-500 dataset showed that our method achieves advanced segmentation performance with Dice similarity coefficients of 93.04% and 89.74%, respectively. Significance. The proposed network provides reliable 3D-to-2D segmentation of retinal vessels, with potential for application in various segmentation tasks that involve projecting the input image. Implementation code: https://github.com/y8421036/LA-Net.

Polyethylenimine Triggers Dll4 Degradation to Regulate Angiogenesis In Vitro.

The Dll4-Notch signaling pathway plays a crucial role in the regulation of angiogenesis and is a promising therapeutic target for diseases associated with abnormal angiogenesis, such as cancer and ophthalmic diseases. Here, we find that polyethylenimine (PEI), a cationic polymer widely used as nucleic acid transfection reagents, can target the Notch ligand Dll4. By immunostaining and immunoblotting, we demonstrate that PEI significantly induces the clearance of cell-surface Dll4 and facilitates its degradation through the lysosomal pathway. As a result, the activation of Notch signaling in endothelial cells is effectively inhibited by PEI, as evidenced by the observed decrease in the generation of the activated form of Notch and expression of Notch target genes Hes1 and Hey1. Furthermore, through blocking Dll4-mediated Notch signaling, PEI treatment enhances angiogenesis in vitro. Together, our study reveals a novel biological effect of PEI and establishes a foundation for the development of a Dll4-targeted biomaterial for the treatment of angiogenesis-related disease.

High-precision retinal blood vessel segmentation based on a multi-stage and dual-channel deep learning network.

Objective.The high-precision segmentation of retinal vessels in fundus images is important for the early diagnosis of ophthalmic diseases. However, the extraction for microvessels is challenging due to their characteristics of low contrast and high structural complexity. Although some works have been developed to improve the segmentation ability in thin vessels, they have only been successful in recognizing small vessels with relatively high contrast.Approach.Therefore, we develop a deep learning (DL) framework with a multi-stage and dual-channel network model (MSDC_NET) to further improve the thin-vessel segmentation with low contrast. Specifically, an adaptive image enhancement strategy combining multiple preprocessing and the DL method is firstly proposed to elevate the contrast of thin vessels; then, a two-channel model with multi-scale perception is developed to implement whole- and thin-vessel segmentation; and finally, a series of post-processing operations are designed to extract more small vessels in the predicted maps from thin-vessel channels.Main results.Experiments on DRIVE, STARE and CHASE_DB1 demonstrate the superiorities of the proposed MSDC_NET in extracting more thin vessels in fundus images, and quantitative evaluations on several parameters based on the advanced ground truth further verify the advantages of our proposed DL model. Compared with the previous multi-branch method, the specificity and F1score are improved by about 2.18%, 0.68%, 1.73% and 2.91%, 0.24%, 8.38% on the three datasets, respectively.Significance.This work may provide richer information to ophthalmologists for the diagnosis and treatment of vascular-related ophthalmic diseases.

CF-Loss: Clinically-relevant feature optimised loss function for retinal multi-class vessel segmentation and vascular feature measurement

Characterising clinically-relevant vascular features, such as vessel density and fractal dimension, can benefit biomarker discovery and disease diagnosis for both ophthalmic and systemic diseases. In this work, we explicitly encode vascular features into an end-to-end loss function for multi-class vessel segmentation, categorising pixels into artery, vein, uncertain pixels, and background. This clinically-relevant feature optimised loss function (CF-Loss) regulates networks to segment accurate multi-class vessel maps that produce precise vascular features. Our experiments first verify that CF-Loss significantly improves both multi-class vessel segmentation and vascular feature estimation, with two standard segmentation networks, on three publicly available datasets. We reveal that pixel-based segmentation performance is not always positively correlated with accuracy of vascular features, thus highlighting the importance of optimising vascular features directly via CF-Loss. Finally, we show that improved vascular features from CF-Loss, as biomarkers, can yield quantitative improvements in the prediction of ischaemic stroke, a real-world clinical downstream task. The code is available at https://github.com/rmaphoh/feature-loss.

Effect of human lactoferrin solution on histological changes of rabbit cornea in alkaline burns

BACKGROUND: Eye burns are a severe injury, accounting for 6.1–38.4% of all ophthalmic diseases. Alkaline burns are the most common and most severe forms of eye burns.
 AIM: Evaluation of the effectiveness of drug topical application based on human lactoferrin in alkaline cornea burns of the 3rd degree in the experiment.
 MATERIAL AND METHODS: The study was conducted on 42 rabbits. It included two groups: the first (without treatment) — alkaline burns during therapy with water for injection (1 drop 3 times a day) — 21 rabbits, the second (experimental) — treatment with lactoferrin solution (concentration 2.5 mg/ml, 1 drop 3 times a day) — 21 rabbits. Alkaline burns were induced by applying a filter paper disc moistened with 5% sodium hydroxide solution to the cornea. 6 eyes of 3 intact animals served as controls. The effectiveness of the drug was evaluated by the rate of closure of the corneal epithelium defect, the time of suppression of the inflammatory reaction in the area of the defect and the limbus, the degree of restoration of the morphological structure of the cornea, as close as possible to the normal rabbit cornea. The obtained data were processed using the methods of variation statistics, the Statistica 10.0 software package. The significance of differences was assessed by calculating the median and interquartile interval. The critical level of significance for statistical criteria was taken as p=0.05.
 RESULTS: From the 3rd day of the study, in the experimental treatment group, there was an acceleration of reepithelialization, restoration of the cornea's own substance, and a more rapid subsidence of inflammation compared to the control group. The thickness of the cornea in the center of the defect in the group without treatment was significantly higher than the values of intact animals at all periods of observation: on the 3rd day after the burn it was 615.99 [450.70–794.07] µm (p=0.000574), reached the maximum by day 7 — it was 1363.16 [907.78–1543.44] µm (p=0.000091), and by day 28 it decreased to 384.38 [376.03–398.14] µm (p=0.0000041). In the group with experimental treatment, the thickness of the cornea in the center of the defect also increased relative to intact animals, starting from the 1st day of pathology, reaching maximum values on the 3rd day — 436.70 [415.57–489.90] µm (p=0.005589). The use of lactoferrin solution in comparison with the first group led to a significant decrease in the thickness of the cornea in the center of the defect on 7th (p=0.039985) and 28th days (p=0.0443).
 CONCLUSION: Local application of lactoferrin solution in alkaline burns of the cornea promotes faster regeneration of the epithelium and restoration of the stroma structure of the rabbit cornea.

Retinal OCT Image Classification Based on CNN-RNN Unified Neural Networks

<p>Computer-aided diagnosis of retinopathy is a hot research topic in the field of medical image classification, where optical coherence tomography (OCT) is an important basis for the diagnosis of ophthalmic diseases. Traditional approaches to multi-label image classification learn independent classifiers for each category and employ ranking or thresholding on the classification results. These techniques, although working well, fail to explicitly exploit the label dependencies in an image. In this paper, two publicly available retinal OCT image datasets are integrated and screened. Then, an end-to-end deep learning algorithmic framework based on CNN-RNN Unified Neural Networks was proposed to automatically and reliably classify six categories of retinal OCT images. Numerical results suggest that the proposed algorithm works well in terms of accuracy, precision, sensitivity and specificity, approaching or even partially surpassing the performance of clinical experts. It is valuable in promoting computer-aided diagnosis towards practical clinical applications and improving the efficiency of clinical diagnosis of retinal diseases.</p> <p> </p>

Big data to guide glaucoma treatment.

Ophthalmology has been at the forefront of the medical application of big data. Often harnessed with a machine learning approach, big data has demonstrated potential to transform ophthalmic care, as evidenced by prior success on clinical tasks such as the screening of ophthalmic diseases and lesions via retinal images. With the recent establishment of various large ophthalmic datasets, there has been greater interest in determining whether the benefits of big data may extend to the downstream process of ophthalmic disease management. An area of substantial investigation has been the use of big data to help guide or streamline management of glaucoma, which remains a leading cause of irreversible blindness worldwide. In this review, we summarize relevant studies utilizing big data and discuss the application of the findings in the risk assessment and treatment of glaucoma.

Application of artificial intelligence in glaucoma care: An updated review.

The application of artificial intelligence (AI) in ophthalmology has been increasingly explored in the past decade. Numerous studies have shown promising results supporting the utility of AI to improve the management of ophthalmic diseases, and glaucoma is of no exception. Glaucoma is an irreversible vision condition with insidious onset, complex pathophysiology, and chronic treatment. Since there remain various challenges in the clinical management of glaucoma, the potential role of AI in facilitating glaucoma care has garnered significant attention. In this study, we reviewed the relevant literature published in recent years that investigated the application of AI in glaucoma management. The main aspects of AI applications that will be discussed include glaucoma risk prediction, glaucoma detection and diagnosis, visual field estimation and pattern analysis, glaucoma progression detection, and other applications.

Retinal Layer Segmentation in OCT Images With Boundary Regression and Feature Polarization.

The geometry of retinal layers is an important imaging feature for the diagnosis of some ophthalmic diseases. In recent years, retinal layer segmentation methods for optical coherence tomography (OCT) images have emerged one after another, and huge progress has been achieved. However, challenges due to interference factors such as noise, blurring, fundus effusion, and tissue artifacts remain in existing methods, primarily manifesting as intra-layer false positives and inter-layer boundary deviation. To solve these problems, we propose a method called Tightly combined Cross-Convolution and Transformer with Boundary regression and feature Polarization (TCCT-BP). This method uses a hybrid architecture of CNN and lightweight Transformer to improve the perception of retinal layers. In addition, a feature grouping and sampling method and the corresponding polarization loss function are designed to maximize the differentiation of the feature vectors of different retinal layers, and a boundary regression loss function is devised to constrain the retinal boundary distribution for a better fit to the ground truth. Extensive experiments on four benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance in dealing with problems of false positives and boundary distortion. The proposed method ranked first in the OCT Layer Segmentation task of GOALS challenge held by MICCAI 2022. The source code is available at https://www.github.com/tyb311/TCCT.

Reliability and accuracy of artificial intelligence ChatGPT in providing information on ophthalmic diseases and management to patients.

To assess the accuracy of ophthalmic information provided by an artificial intelligence chatbot (ChatGPT). Five diseases from 8 subspecialties of Ophthalmology were assessed by ChatGPT version 3.5. Three questions were asked to ChatGPT for each disease: what is x?; how is x diagnosed?; how is x treated? (x = name of the disease). Responses were graded by comparing them to the American Academy of Ophthalmology (AAO) guidelines for patients, with scores ranging from -3 (unvalidated and potentially harmful to a patient's health or well-being if they pursue such a suggestion) to 2 (correct and complete). Accuracy of responses from ChatGPT in response to prompts related to ophthalmic health information in the form of scores on a scale from -3 to 2. Of the 120 questions, 93 (77.5%) scored ≥ 1. 27. (22.5%) scored ≤ -1; among these, 9 (7.5%) obtained a score of -3. The overall median score amongst all subspecialties was 2 for the question "What is x", 1.5 for "How is x diagnosed", and 1 for "How is x treated", though this did not achieve significance by Kruskal-Wallis testing. Despite the positive scores, ChatGPT on its own still provides incomplete, incorrect, and potentially harmful information about common ophthalmic conditions, defined as the recommendation of invasive procedures or other interventions with potential for adverse sequelae which are not supported by the AAO for the disease in question. ChatGPT may be a valuable adjunct to patient education, but currently, it is not sufficient without concomitant human medical supervision.

Applications of deep learning for detecting ophthalmic diseases with ultrawide-field fundus images.

To summarize the application of deep learning in detecting ophthalmic disease with ultrawide-field fundus images and analyze the advantages, limitations, and possible solutions common to all tasks. We searched three academic databases, including PubMed, Web of Science, and Ovid, with the date of August 2022. We matched and screened according to the target keywords and publication year and retrieved a total of 4358 research papers according to the keywords, of which 23 studies were retrieved on applying deep learning in diagnosing ophthalmic disease with ultrawide-field images. Deep learning in ultrawide-field images can detect various ophthalmic diseases and achieve great performance, including diabetic retinopathy, glaucoma, age-related macular degeneration, retinal vein occlusions, retinal detachment, and other peripheral retinal diseases. Compared to fundus images, the ultrawide-field fundus scanning laser ophthalmoscopy enables the capture of the ocular fundus up to 200° in a single exposure, which can observe more areas of the retina. The combination of ultrawide-field fundus images and artificial intelligence will achieve great performance in diagnosing multiple ophthalmic diseases in the future.

Deep learning-based classification of eye diseases using Convolutional Neural Network for OCT images

Deep learning shows promising results in extracting useful information from medical images. The proposed work applies a Convolutional Neural Network (CNN) on retinal images to extract features that allow early detection of ophthalmic diseases. Early disease diagnosis is critical to retinal treatment. Any damage that occurs to retinal tissues that cannot be recovered can result in permanent degradation or even complete loss of sight. The proposed deep-learning algorithm detects three different diseases from features extracted from Optical Coherence Tomography (OCT) images. The deep-learning algorithm uses CNN to classify OCT images into four categories. The four categories are Normal retina, Diabetic Macular Edema (DME), Choroidal Neovascular Membranes (CNM), and Age-related Macular Degeneration (AMD). The proposed work uses publicly available OCT retinal images as a dataset. The experimental results show significant enhancement in classification accuracy while detecting the features of the three listed diseases.

A facile method to construct ZIF-8 MOFs on contact lens for high antibiotics loading and self-defensive release

Eye trauma, ophthalmic surgery and contact lens (CL) wearing cause corneal infection and bacterial biofilms formation, which further lead to corneal ulcer and perforation. In view of the low bioavailability of antibiotic eye drops and the clinical problems that cause bacterial resistance, we used pH sensitive zeolitic imidazolate framework-8 metal–organic frameworks (ZIF-8 MOFs) to load broad-spectrum antibacterial drugs levofloxacin (LEVO). After the surface modification by hyaluronic acid (HA), the nanomaterial surface was negatively charged. A simple amination treatment was used to endow CL surface with amino groups to combine the negatively charged nano drug loading system through electrostatic forces. The bacteriostatic ring of ZIF-8@LEVO@HA-PEI modified CL (ZLHP-CL) was 34 mm, which could be owing to the high drug loading capacity of ZIF-8 (0.65 mg/cm2). The bactericidal function was further confirmed by shake flask method and live/dead bacterial staining assay against S. aureus, S. epidermidis and E. coli. It is worth mentioning that ZLHP-CL showed significant pH sensitivity to the bacterial infection, which released drug rapidly at pH 5.5 and hardly released drug at pH 7.4. In the bacterial biofilm’s formation experiment, it was found that ZLHP-CL obviously inhibited bacterial adhesion and biofilms formation. Furthermore, it was proved in infectious keratitis animal that ZLHP-CL could kill the bacteria and reduce the inflammation reaction, which showed advantages in corneal tissue repair and light transmittance maintenance. In this work, based on nanotechnology, a facile CL drug loading technology is developed, which can be used to sustained release of various drugs for ophthalmic diseases treatment.

Targeting the Ophthalmic Diseases Using Extracellular Vesicles 'Exosomes': Current Insights on Their Clinical Approval and Present Trials.

Ophthalmic diseases encompass a diverse range of conditions, each necessitating tailored treatment strategies. In the realm of ophthalmic research and therapeutic interventions, various subtypes of exosomes are being explored for their regenerative, neuroprotective, and anti-inflammatory properties. Exosomes have garnered increasing attention as promising therapeutic vehicles due to their natural role in cell-to-cell communication and targeted delivery capabilities. Derived from cells, these small vesicles facilitate the transportation of numerous molecules between cells, offering advantages such as low immunogenicity, stability, and precise cell targeting. These inherent qualities make exosomes an enticing avenue for advancing treatment options for ophthalmic diseases. While ongoing research and clinical applications continue to evolve, several exosome subtypes have demonstrated potential for addressing various ophthalmic conditions, including glaucoma, age-related macular degeneration, retinal degenerative disorders, and ocular inflammatory conditions.

AI‐based studies using fundus images

The artificial intelligence (AI)/deep learning (DL) algorithms were shown to detect diseases based on image analysis, including retinal diseases from fundus photos and optical coherence tomography (OCT) scans, lung diseases from chest radiographs, and skin disorders from skin photos. It was also shown that retinal photos can be used to identify risk factors related with cardiovascular disorders, including blood pressure, smoking and body mass index.The fundus picture was shown to source data for developing AI/DL algorithms detecting many ophthalmic diseases and pathologies, including diabetic retinopathy, diabetic macular edema, retinal artery and vein occlusion, macular hole, epiretinal membrane, AMD, glaucoma, retinopathy of prematurity, myopia, myopic macular degeneration, hyperopia, optic disc edema, optic nerve atrophy, central serous retinopathy, myelinated nerve fibres, and retinitis pigmentosa. Moreover, fundus pictures were also used to develop many AI/DL algorithms detecting non ophthalmic diseases and pathologies, including Alzheimer and Parkinson disease, risk of stroke, risk of cardiac infarction, systemic hypertension, chronic renal disease, peripheral arterial disease, and anaemia.