Common Eye Diseases Research Articles

Optic Disc Segmentation in Human Retina Images Using a Meta Heuristic Optimization Method and Disease Diagnosis with Deep Learning

Glaucoma is a common eye disease that damages the optic nerve and leads to loss of vision. The disease shows few symptoms in the early stages, making its identification a complex task. To overcome the challenges associated with this task, this study aimed to tackle the localization and segmentation of the optic disc, as well as the classification of glaucoma. For the optic disc segmentation, we propose a novel metaheuristic approach called Grey Wolf Optimization (GWO). Two different approaches are used for glaucoma classification: a one-stage approach, in which the whole image without cropping is used for classification, and a two-stage approach. In the two-stage approach, the optic disc region is detected using the You Only Look Once (YOLO) detection algorithm. Once the optic disc region of interest (ROI) is identified, glaucoma classification is performed using pre-trained convolutional neural networks (CNNs) and vision transformation techniques. In addition, both the one-stage and the two-stage approaches are applied in combination with the pre-trained CNN using the Random Forest algorithm. In segmentation, GWO achieved an average sensitivity of 96.04%, a specificity of 99.58%, an accuracy of 99.39%, a DICE coefficient of 94.15%, and a Jaccard index of 90.4% on the Drishti-GS dataset. For classification, the proposed method achieved remarkable results with a high-test accuracy of 100% and 88.18% for hold-out validation and three-fold cross-validation for the Drishti-GS dataset, and 96.15% and 93.84% for ORIGA with hold-out and five-fold cross-validation, respectively. Comparing the results with previous studies, the proposed CNN model outperforms them. In addition, the use of the Swin transformer shows its effectiveness in classifying glaucoma in different subsets of the data.

Comparative analysis of the effect of drugs lowering intraocular pressure on a primary culture of human corneal epithelium and A549 immortalized cell line

Background. Glaucoma as one of the most common eye diseases can be a comorbid condition of an epithelial corneal defect of various etiologies. Maintaining an optimal level of ophthalmotonus includes the prescription of antiglaucoma drugs, including benzalkonium chloride-preserved drugs.The aim of the study. To compare the effect of antiglaucoma drugs, as well as benzalkonium chloride (BC), on a primary culture of human corneal epithelium and A549 immortalized cell line.Methods. The effect of brimonidine, dorzolamide and timolol (1/100, 1/50, 1/20 and 1/10 dilutions; exposure 24 hours) on a monolayer of a human corneal epithelial primary culture and A549 immortalized cell line was assessed by structural changes in cells (phase-contrast microscopy) and MTT assay data. The cytotoxic effect of BC was studied in concentrations corresponding to its content in these dilutions of the antiglaucoma drug. Using a model of a linear defect in the monolayer of a corneal epithelial primary culture and A549 immortalized cell line, the effects of brimonidine, dorzolamide and timolol (1/100 and 1/20 dilutions; exposure 48 hours) were assessed by cell migration activity.Results. Among drugs (BC-free), dorzolamide (1/50, 1/20 and 1/10 dilutions) causes minor structural changes in human corneal epithelium and A549 immortalized cell line; timolol (1/100, 1/50, 1/20 and 1/10 dilutions) causes minor structural changes in A549 immortalized cell line. Structural changes in both types of cells, a decrease in their metabolic and migration activity occur under the influence of dorzolamide, brimonidine and timolol (BC-preserved) in 1/100, 1/50, 1/20 and 1/10 dilutions. BC at the studied concentrations exhibits a similar effect.Conclusion. The cytotoxic effect of antiglaucoma drugs is caused by the presence of BC in their composition. Despite similar morphofunctional changes in cells, A549 immortalized cell line is more resistant to the effects of drugs than the human corneal epithelial primary culture. When using it as a cellular model, it is advisable to change the experimental conditions (duration of exposure and concentration of the studied drug).

Prevalence of Eye Diseases of the Elderly in University Eye Clinic

Objective: To assess the prevalence of eye diseases among elderly receiving care at a University Healthcare eye service. Materials and Methods: A retrospective review of medical records was conducted for elderly patients attending Rangsit University Healthcare’s eye services between 2020 and 2021. Data collected included age, gender, underlying systemic diseases, refraction data, and results of comprehensive eye examinations. Logistic regression analysis was performed to investigate the associations between age, gender, and eye diseases. Results: Out of 22,563 patients that attended the eye service, 1,452 were aged 60 years and older. The most common eye diseases observed were uncorrected refractive errors at 71.14%, followed by cataracts including pseudophakia at 69.77%, glaucoma at 21.9%, and posterior vitreous detachment at 14.6%. Additionally, pinguecula, pterygium, and lid disorders were presented in 10.67% of cases, dry eye 8.47%, retinal breaks 6.89%, epiretinal membrane 5.85%, diabetic retinopathy 4.75%, and age-related macular degeneration 4.13%. The prevalence of retinal breaks, epiretinal membrane, diabetic retinopathy, age-related macular degeneration, glaucoma, and corneal diseases increased with age while pinguecula, pterygium, and lid disorders showed an inverse association with age. Cataracts, posterior vitreous detachment, and dry eyes were more common in women, whereas, pinguecula, pterygium, lid disorders, and corneal diseases were more prevalent in men. Refractive error, glaucoma, and age-related macular degeneration showed no gender difference. Compared to the previous national surveys in Thailand, posterior segment eye diseases were more prevalent in the present study. Conclusion: Uncorrected refractive errors, cataracts, and glaucoma were the most prevalent eye diseases observed in the elderly patients, consistent with the previous national surveys. However, the present study identified an increased prevalence of posterior segment eye diseases. Early screening and referral by optometrists for timely treatment by ophthalmologists are crucial in preventing visual loss and achieving better visual outcomes while optimizing resource utilization.

Comparison of the recovery characteristics for canine corneal ulcer treated with corneoconjunctival transposition or conjunctival autografts

Corneal ulceration induced by Staphylococcus pseudintermedius (S. pseudintermedius) is a common clinical eye disease. Antibiotics combined with corneoconjunctival transposition (CCT) or conjunctival autografts (CA) are often used, but the recovery characteristics are still unknown. In this experiment, canine corneal ulcer models induced by S. pseudintermedius and treated with levofloxacin eye drops (LED) were created. The models were used to compare the recovery characteristics of CCT and CA, combined with LED, by clinical observation, histopathology, and cytokine expression detected by qRT-PCR analysis. The results showed that the ulcerative cornea with only LED treatment perforated after 48 h. The mRNA expression of TLR2, IL-1β, IL-6, IL-8, and TNF-α genes was significantly elevated on 14, 28, and 35 days after the surgery compared to normal (p < 0.01). On day 42, the inflammatory damage had resolved, but the corneal transparency and arrangement of collagen fibrils in the CCT group were higher than those in the CA group. The mRNA expression of EGF, FGF, TGF-β1 and VEGF genes increased significantly (p < 0.01), mostly until day 42, proving that CCT and CA surgery contributed to the corneal recovery, and relieved the inflammatory reaction, with the elimination of corneal cicatrices needing a period of reconstruction. Therefore, this study has provided, for the first time, the method for establishing a canine corneal ulcer model induced by S. pseudintermedius. More importantly, the recovery of canine ulcerative corneas with CCT or CA surgery is reported for the first time.

Ocular disorders in a tertiary Hospital in South East Nigeria

The object of this study was to determine the pattern of ocular disease patients that presented to the Enugu State University of Science and Technology Teaching Hospital, Park Lane, Enugu, Nigeria. A retrospective study was conducted, where the medical case notes of patients who had ocular examinations, other clinical examinations, and investigations from April 2022 through December 2022 were retrieved. There were a total of 1145 patients, with a male population of 476 (41.6%), while females were 669 (58.4%). The mean age of the study population was 40.8 years, with a Standard Deviation (SD) of 22.4. A total of 924 (80.7%) participants were adults while children (aged 16 years and below) were 221 in number and constituted 19.3% of the study population. The most common eye disease was refractive error, which occurred in 266 (23.2%) of the participants; it was followed by allergic eye diseases, 201 (17.6%), cataract, 170 (14.9%), glaucoma, 111 (9.7%), and trauma to the eye, 94 (8.2%). Among the children, the most common eye disease seen were allergic eye diseases, 78 (35.3%), followed by refractive error, 70 (31.7%), trauma, 21 (9.5%), cataract, 11 (5.0%), while infections of the eye were 10 (4.5%). The most common eye disease among the adult participants was refractive error, 196 (21.1%), and then cataract, 159 (17.2%), allergic eye diseases, 123 (13.3%), glaucoma, 105 (11.4%), and retinal pathologies, 82 (8.9%). The most common causes of eye diseases in this study were refractive error, allergic eye diseases, cataracts, glaucoma, and trauma. Health education at community and primary health care level could help to reduce the number of people with avoidable causes of visual impairment.

AWARENESS AND KNOWLEDGE TOWARDS COMMON EYE DISEASES OF CITIZENS IN MEDINA CITY, SAUDI ARABIA

Objective: The study aimed to assess the extent of knowledge regarding common eye diseases namely cataracts, diabetic retinopathy, glaucoma, refractive errors, and eye traumas among citizens of Medina City, Saudi Arabia. Subjects and Methods: A total of 288 citizens (≥18 years old) of Medina city were selected and asked to answer online questionnaires regarding their knowledge on common eye diseases. The questionnaire includes demographics, awareness, and source of information of eye diseases. Results: Overall, the 283 participants have obtained a good mean general knowledge on cataract, strabismus, and refractive error, while nearly intermediate mean general knowledge about diabetic retinopathy and glaucoma. Knowledge scores of the participants were found to be highest for cataract (64.25±27.6), while lowest for glaucoma (47.76±34.3). The common source of information regarding eye disease were their family members and friends (33.3%, n=96). In term of socio-demographic factors, male gender was found to be the common factor that significantly influence the awareness of participants towards cataract, retinopathy, glaucoma, and refractive errors, while presence of eye problem was observed to significantly influence the knowledge of respondents towards retinopathy, glaucoma, and strabismus. Conclusion: This study concludes that proper and extended awareness and knowledge, such as creating health programs and effective health education, towards eye diseases must be imposed to help mitigate the cases of eye health diseases.

Molecular identification of the bacterium acute conjunctivitis by the method of sequensing gen 16S rRNA

Conjunctivitis is the most common eye disease, characterized by contextual inflammation, which can be caused by bacteria. The diagnosis of conjunctivitis is established based various factors, including the patients’s medical history, onset of eye symptoms, non-ocular symptoms, previous disease history, family medical history, allergies, and physical examination of the eyes, which may include assessments of visual acuity and vision field. Some cases of conjunctivitis require laboratory examinations to confirm the diagnosis. These may include cytological examination with Giemsa staining, Chlamydia Diagnostic Test, Nucleic acid amplification tests (NAATs) or Polymerase chain reaction (PCR), and microbiological tests. Microbiological examination helps identify the bacteria responsible for conjunctivitis and aids in treatment by prescribing antibiotics to suppress the growth of the infecting bacteria. While identification tests are not routinely performed, researchers often seek to determine the specific type of bacteria causing conjunctivitis infections, which may require several laboratory tests. Bacterial virulence plays a significant role, with genetic mutations potentially leading to severe infections of varying severity. Virulence genes encode proteins that express pathogenic properties. The species responsible for conjunctivitis can be definitively identified definitively through microbiological examination, utilizing methods such as the 16S Ribosomal RNA Sequencing (rRNA) technique, known for its accuracy and speed. This study aims to analyze the results of rRNA sequencing in cases of acute bacterial conjunctivitis caused by 16S rRNA genotyping. The research employed an exploratory metholodolot, with the results analyzed using the The Basic Local Alignment Search Tool (BLAST) tracking program database on the National Center for Biotechnology Information (NCBI) website. The findings revealed that Sphingomonas paucimobilis encoded the 16S rRNA using Universal Primary 27 F and 1492 R, obtained in a sequence size of 1351bp. The isolate demonstrated similarities to Sphingomonas paucimobilis.

Optic disc and cup segmentation for glaucoma detection using Attention U-Net incorporating residual mechanism.

Glaucoma is a common eye disease that can cause blindness. Accurate detection of the optic disc and cup disc is crucial for glaucoma diagnosis. Algorithm models based on artificial intelligence can assist doctors in improving detection performance. In this article, U-Net is used as the backbone network, and the attention and residual modules are integrated to construct an end-to-end convolutional neural network model for optic disc and cup disc segmentation. The U-Net backbone is used to infer the basic position information of optic disc and cup disc, the attention module enhances the model's ability to represent and extract features of optic disc and cup disc, and the residual module alleviates gradient disappearance or explosion that may occur during feature representation of the neural network. The proposed model is trained and tested on the DRISHTI-GS1 dataset. Results show that compared with the original U-Net method, our model can more effectively separate optic disc and cup disc in terms of overlap error, sensitivity, and specificity.

Public Awareness of Common Age-Related Eye Diseases in Northern Saudi Arabia.

This population-based descriptive survey was conducted on 411 Saudi adults aged 18-70 residing in Arar City,Saudi Arabia, over six months from September 2023. Random sampling was employed, and awareness levels were assessed using a pre-designed questionnaire. Data analysis was performed using Statistical Product and Service Solutions (SPSS, version 20.0; IBM SPSS Statistics for Windows, Armonk, NY). Among the 411 participants, 225 (54.7%) were aged 18-29 years, 312 (76%) were females, and 299 (72.6%) held a bachelor's degree or diploma. Regarding awareness, 71.3% knew about cataracts, but nearly half erroneously believed it could be treated solely medically. For diabetic retinopathy (DR), 366 (89%) recognized lifestyle modifications, and 378 (92%) understood screening as preventive measures. Most participants understood prevention methods for all four diseases, but misconceptions about treatment options were observed. Awareness of glaucoma and age-related macular degeneration (ARMD) was lower. No significant age-related differences were found in awareness, except for cataracts (P = 0.001). Education significantly influenced awareness of cataracts, DR, and glaucoma (P = 0.001, 0.013, and 0.008, respectively), but not ARMD (P = 0.606). The study found that the internet is the primary source of information on AREDs for most participants, except for cataracts, where friends and relatives are prominent. The study reveals varying awareness levels of AREDs among Saudi adults. Although most participants understood preventive measures, misconceptions about treatment underscore the need for accurate education channels. Healthcare professionals must ensure information reliability to effectively combat misinformation and enhance awareness of AREDs.

Cataract classification and grading in federated learning environment using CNN based Modified-ResNet-50

Cataract, a common eye disease, causes lens opacification, which can lead to blindness. Early cataract detection in a privacy-preserving approach has led us to investigate the concept of Federated Learning (FL) and its prominent technique, known as Federated Averaging (FedAVG). Federated learning has the potential to solve the privacy issues by allowing data servers to train their models natively and distribute them without invading patient confidentiality. This research introduces an interactive federated learning framework that permits multiple medical institutions to screen cataract from split lamp images utilising convolutional neural network (CNN) without sharing patient data, as well as grade normal, mild, moderate, and severe cataracts. The CNN is developed based on Modified-ResNet-50 and FedAVG technique could achieve relatively high accuracy. The experimental results demonstrate that the proposed modification reduces the processing time to a greater extent.

Awareness of Common Eye Diseases Among College Students at A Saudi University

Introduction: Many eye conditions, including cataract, glaucoma, and diabetic retinopathy, are among the major causes of blindness in the world. There were many studies on the subject worldwide but fewer in the Middle East; hence, the subject needs more investigation. This research is significant as it fills the gap in the literature on the awareness of common eye diseases in Saudi Arabia. This study was carried out to assess the awareness of common eye diseases among college students at a Saudi University. Methodology: This was a cross-sectional study, including college students at Prince Sattam bin Abdulaziz University (PSAU), Saudi Arabia. Data was collected using a self-administered questionnaire and was analyzed using the SPSS program. Results: Our study included 579 participants. 62.7% of participants reported no family history of eye diseases, and among those who did, dry eye was the most frequently mentioned condition, with 27.8%. Cataract, glaucoma, and diabetic retinopathy had lower reported family histories at 12.3%, 5.9%, and 3.5%, respectively. Concerning awareness, 60.3% of participants were aware of dry eye, followed by cataract (38.7%) and glaucoma (33.3%). However, 26.9% reported no awareness of any eye disease. The internet emerged as the most significant source of information (46.7%), while family, friends, and relatives were also prominent sources (35.4%). Regarding knowledge of specific eye diseases, there were varying awareness levels. For example, 58.4% recognized cataract as treatable, and 42.5% knew glaucoma could cause blindness. Conclusion: A significant proportion of participants exhibited awareness of conditions like dry eye, cataract, and glaucoma; there is room for improvement. Study findings underscore the importance of educational interventions aimed at enhancing awareness, especially for students in non-medical disciplines.

Progress in clinical research and applications of retinal vessel quantification technology based on fundus imaging.

Retinal blood vessels are the only directly observed blood vessels in the body; changes in them can help effective assess the occurrence and development of ocular and systemic diseases. The specificity and efficiency of retinal vessel quantification technology has improved with the advancement of retinal imaging technologies and artificial intelligence (AI) algorithms; it has garnered attention in clinical research and applications for the diagnosis and treatment of common eye and related systemic diseases. A few articles have reviewed this topic; however, a summary of recent research progress in the field is still needed. This article aimed to provide a comprehensive review of the research and applications of retinal vessel quantification technology in ocular and systemic diseases, which could update clinicians and researchers on the recent progress in this field.

A systematic literature review for evaluation of knowledge, attitude, and self-care practice regarding common eye diseases in the healthy general population.

Increasing people's knowledge and then changing their attitude and practice with the aim of taking care of their eye health are very important. Considering the importance of the mentioned topic, the main goal of this study was to evaluate the knowledge, attitude, and practice about eye diseases in the general population of the world in the form of a systematic literature review. This study was a systematic literature review study, and to do it, a systematic search was conducted in internationally available databases including Web of Science, ScienceDirect, Scopus, PubMed, and Google Scholar in the time range of 1998 to 2023. Finally, considering the inclusion and exclusion criteria of the study, the results of 18 articles were extracted. The findings showed that in general, people's level of knowledge about glaucoma was lower compared to other eye diseases, and the level of knowledge of men and women about eye diseases was different. In addition, the results showed that there was significant relationship between age and knowledge of various eye diseases. The results of all evaluated studies showed that people with higher education have more knowledge about eye diseases. Based on this, it can be concluded that as glaucoma is one of the most important causes of blindness worldwide, it is necessary to plan to increase the level of public knowledge to recognize the symptoms and complications of this disease. In addition to that, it is necessary to increase people's advertisement by ophthalmology centers and eye specialists about the use of glasses and also to encourage people to visit the eye physician regularly.

Research progress on ocular complications caused by type 2 diabetes mellitus and the function of tears and blepharons.

The purpose of this study was to explore the related research progress of ocular complications (OCs) caused by type 2 diabetes mellitus (T2DM), tear and tarsal function, and the application of deep learning (DL) in the diagnosis of diabetes and OCs caused by it, to provide reference for the prevention and control of OCs in T2DM patients. This study reviewed the pathogenesis and treatment of diabetes retinopathy, keratopathy, dry eye disease, glaucoma, and cataract, analyzed the relationship between OCs and tear function and tarsal function, and discussed the application value of DL in the diagnosis of diabetes and OCs. Diabetes retinopathy is related to hyperglycemia, angiogenic factors, oxidative stress, hypertension, hyperlipidemia, and other factors. The increase in water content in the corneal stroma leads to corneal relaxation, loss of transparency, and elasticity, and can lead to the occurrence of corneal lesions. Dry eye syndrome is related to abnormal stability of the tear film and imbalance in neural and immune regulation. Elevated intraocular pressure, inflammatory reactions, atrophy of the optic nerve head, and damage to optic nerve fibers are the causes of glaucoma. Cataract is a common eye disease in the elderly, which is a visual disorder caused by lens opacity. Oxidative stress is an important factor in the occurrence of cataracts. In clinical practice, blood sugar control, laser therapy, and drug therapy are used to control the above eye complications. The function of tear and tarsal plate will be affected by eye diseases. Retinopathy and dry eye disease caused by diabetes will cause dysfunction of tear and tarsal plate, which will affect the eye function of patients. Furthermore, DL can automatically diagnose and classify eye diseases, automatically analyze fundus images, and accurately diagnose diabetes retinopathy, macular degeneration, and other diseases by analyzing and processing eye images and data. The treatment of T2DM is difficult and prone to OCs, which seriously threatens the normal life of patients. The occurrence of OCs is closely related to abnormal tear and tarsal function. Based on DL, clinical diagnosis and treatment of diabetes and its OCs can be carried out, which has positive application value.

Utilizing visual symptoms to distinguish dry eye from glaucoma, cataract, and suspect glaucoma patients: a cross-sectional study

BackgroundThe diagnosis of dry eye and other common ophthalmological conditions can be supported using patient reported symptoms, which is increasingly useful in contexts such as telemedicine. We aim to ascertain visual symptoms that differentiate dry eye from cataract, glaucoma, or glaucoma suspects.MethodsAdults with dry eye, glaucoma, cataract, and suspected glaucoma (controls) completed a questionnaire to rate the frequency and severity of 28 visual symptoms. Univariate, followed by multivariable logistic regression with backward stepwise selection (p < 0.05), determined the individual symptoms and set of symptoms best distinguishing dry eye from each of the other conditions.ResultsMean age of 353 patients (94 glaucoma suspect controls, 79 glaucoma, 84 cataract, and 96 dry eye) was 64.1 years (SD = 14.1); 67% were female and 68% White. Dry eye patients reported more frequent light sensitivity (OR = 15.0, 95% CI = 6.3–35.7) and spots in vision (OR = 2.8, 95% CI = 1.2–6.3) compared to glaucoma suspect controls. Compared to glaucoma patients, dry eye patients experienced more frequent light sensitivity (OR = 9.2, 95% CI = 2.0–41.7), but less frequent poor peripheral vision (OR = 0.2, 95% CI = 0.06–0.7), difference in vision between eyes (OR = 0.09, 95% CI = 0.01–0.7), and missing patches of vision (OR = 0.06, 95% CI = 0.009–0.3). Compared to cataract patients, dry eye patients reported more frequent spots in vision (OR = 4.5, 95% CI = 1.5–13.4) and vision variability across the week (OR = 4.7, 95% CI = 1.2–17.7) and were less likely to report worsening vision (OR = 0.1, 95% CI = 0.03–0.4) and blindness (OR = 0.1, 95% CI = 0.02–0.8).ConclusionVisual symptoms may serve as a complementary tool to distinguish dry eye from various ocular conditions, though the symptoms that best distinguish dry eye differ across comparisons. Differentiating how patients visually perceive common eye diseases may be used in a variety of clinical settings to rule out specific conditions.

New applications and analyses of ERGs

The electroretinogram (ERG) represents the summed electrical response of retinal neurons to light stimuli and allows direct non‐invasive assessment of human retinal function. Over recent decades there have been numerous developments in our understanding of the cellular basis of different ERG components as well as in techniques of recording and analysing the waveforms elicited by both standard and novel stimulus protocols. Portable devices are now available with automated stimulus adjustment according to pupil diameter, thus permitting non‐mydriatic recordings to be obtained rapidly in the outpatient clinic. Non‐standard stimulus protocols have been devised, for example to selectively isolate rod or cone‐driven contributions to dark‐adapted responses, or to track changes in rod and cone system sensitivity during retinal light and dark adaptation. New methods of analysis have been developed, including fitting of mathematical models to ERGs, application of machine‐learning techniques and interrogation of genetic associations with ERG parameters, the latter potentially shedding light on the effects of common genetic variants in the population. Many of these developments will be discussed, together with insights yielded into retinal physiology and pathophysiology (in both rare and common eye disease), concluding with a discussion of anticipated future investigations and applications in the field.

Molecular changes in intraocular fluid: implications for myopia.

Myopia is the most common eye disease in the world which is caused by a mismatch between the optical power of the eye and its excessive axial length. Scleral remodeling, oxidative stress, inflammation, pathological states of angiogenesis and fibrosis and metabolism are closely associated with the onset and progression of myopia and the pathological changes that may ultimately result. Intraocular fluid is a collective term for the fluid within the eye, and changes in its composition can reflect the physiological and pathological status within the eye, with aqueous humor and vitreous being the commonly tested specimens. Recent studies have revealed potential changes in a variety of molecules in intraocular fluid during myopia progression. Abnormal expression of these molecules may reflect different stages of myopia and provide new perspectives for disease monitoring and treatment. Therefore, in this review, we systematically review the molecular changes in intraocular fluid associated with myopia, as well as the possible mechanisms, with a view to informing basic myopia research and clinical work.

Review of Eye Diseases Detection and Classification Using Deep Learning Techniques

Automated diagnosis of eye diseases using machine and deep learning models has become increasingly popular. Glaucoma, cataracts, diabetic retinopathy, Myopia, and age-related macular degeneration are common eye diseases that can cause severe damage. It is crucial to detect eye diseases early to prevent any potentially serious consequences. Early detection of eye disease is vital for effective treatment. Doing in-depth reading to identify any potential signs of eye disease is highly recommended. This paper will review all machine learning models built to detect and classify eye diseases in addition to helping grasp all limitations and challenges in this field. Recognizing eye diseases is a difficult task that typically requires several years of medical experience. This research is to be conducted to serve as a starting point for finding the most versatile solution. This research aims to review eye disease classification using deep learning models, including VGG16, ResNet, and Inception. The general classification model consists of these steps: The first step is to collect the globally obtainable datasets for the eye disease and pre-process them to ensure the generalization of experiments. The goal is to train the model to recognize disease symptoms instead of tweaking the outcomes for a specific dataset section. With the successful deployment of deep learning techniques for image classification and object recognition, research is now directed towards deep learning techniques instead of traditional handcrafted methods. One possible solution for the eye diseases classification challenge is to use a pre-trained deep CNN model for representation and feature extraction. This solution can be followed by classifier methods, such as support vector machines (SVM), multilayer perceptron (MLP), etc. It has been detected that CNN-based methods learned on large-scale marked datasets can be used for eye disease classification tasks with limited training datasets.

Enhancing the Inhibition of Corneal Neovascularization Efficacy by Self-Assembled into Supramolecular Hydrogel of Anti-Angiogenic Peptide.

Corneal neovascularization (CNV) is a common eye disease that leads to blindness. New treatment strategies are urgently needed due to the limitations of current treatment methods. We report the synthesis of peptide Nap-FFEEPCAIWF ( Comp.3 ) via chemical conjugation of Nap-FFEE ( Comp.2 ) to antiangiogenic peptide PCAIWF (Comp.1). Comp.3 self-assembled into a hydrogel ( gel of 3 ) composed of nanofibers, which enhanced the antiangiogenic function of the epitope. We developed a novel peptide with an amphiphilic framework, Comp.3 , which could self-assemble into a supramolecular hydrogel with a well-ordered nanofiber structure. The nanofibers exhibited good biocompatibility with corneal epithelial cells, presenting a promising strategy to enhance the efficacy of free peptide-based drugs in the treatment of ocular vascular diseases, such as CNV and other angiogenesis-related diseases. Nap-FFEEPCAIWF nanofibers provide an alternative approach to enhancing the therapeutic efficiency of free peptide-based drugs against ocular vascular diseases.

Application and visualization study of an intelligence-assisted classification model for common eye diseases using B-mode ultrasound images.

Conventional approaches to diagnosing common eye diseases using B-mode ultrasonography are labor-intensive and time-consuming, must requiring expert intervention for accuracy. This study aims to address these challenges by proposing an intelligence-assisted analysis five-classification model for diagnosing common eye diseases using B-mode ultrasound images. This research utilizes 2064 B-mode ultrasound images of the eye to train a novel model integrating artificial intelligence technology. The ConvNeXt-L model achieved outstanding performance with an accuracy rate of 84.3% and a Kappa value of 80.3%. Across five classifications (no obvious abnormality, vitreous opacity, posterior vitreous detachment, retinal detachment, and choroidal detachment), the model demonstrated sensitivity values of 93.2%, 67.6%, 86.1%, 89.4%, and 81.4%, respectively, and specificity values ranging from 94.6% to 98.1%. F1 scores ranged from 71% to 92%, while AUC values ranged from 89.7% to 97.8%. Among various models compared, the ConvNeXt-L model exhibited superior performance. It effectively categorizes and visualizes pathological changes, providing essential assisted information for ophthalmologists and enhancing diagnostic accuracy and efficiency.