Eye Specialists Research Articles

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.

Post-fever Retinitis With a Positive Weil-Felix Test: A Study From a Tertiary Center in South India.

Background Post-fever retinitis (PFR) is reported two to six weeks after fever and affects one or both eyes. Rickettsial fever is one of the most common causes of PFR. This study aimed to report the clinical features and treatment outcomes of PFR cases with a positive Weil-Felix test. Methodology In this study, we collected demographic data, fever details, eye symptoms, ocular examination findings, optical coherence tomography (OCT) and fundus fluorescein angiography findings, laboratory findings, and length of follow-up of consecutive PFR cases with a positive Weil-Felix test. All cases were treated with oral doxycycline and prednisolone. Final best-corrected visual acuity (BCVA) and ocular examination findings were recorded. Visual field examination and follow-up OCT results were noted if available. Results A total of nine patients (eight males) with a mean age of 39.1 years with fever before ocular symptoms and positive Weil-Felix test were included.Six patients had bilateral disease. The mean initial and final BCVA in the affected eyes was 1.16 and 0.35 logMAR units, respectively (p < 0001). All 15 eyes had typical white retinitis patches and retinal hemorrhages which resolved after treatment. OCT showed hyperreflectivity and inner retinal disorganization over retinitis patches. White subretinal lines were noted in three patients and retinal nerve fiber bundle defect with corresponding visual field defect was seen in one eye. Conclusions In this study, PFR due to rickettsia infections has been reported from our region for the first time. Hence, eye specialists in the region should be aware of this entity.

Ancient Egyptian Ophthalmology: Eye Diseases and Eye Specialists

Ancient Egyptian Ophthalmology: Eye Diseases and Eye Specialists

Instances of ocular findings in transgender patients undergoing hormonal therapy

PurposeTo describe the ophthalmological manifestations in transgender patients on gender-affirming hormone therapy. MethodsA retrospective chart review study was conducted. Female-to-male (FTM) and male-to-female (MTF) transgenders on gender-affirming hormone therapy evaluated at a single center were included. Candidates were collected using a phrase-identifying search tool within the electronic medical record system. Descriptive analyses were conducted to report the demographics, hormonal therapies, clinical findings, and visual outcomes. ResultsA total of 17 patients were included, seven were FTM, and ten were MTF transgenders. The median age was 26.0 years (range; 20.0–30.0) in the FTM group and 35.0 years (range; 23.0–67.0) in the MTF group. Testosterone therapy in FTM patients comprised 30–60 mg of intramuscular injections weekly or 50 mg of transdermal gel daily. MTF patients used mainly 2–4 mg of estradiol and 100–300 mg of spironolactone tablets daily. A total of 27 eyes were affected, 12 in FTM and 15 in MTF patients. The median visual acuity was 20/25 in FTM (range; 20/20-20/60) and 20/25 in MTF (range; 20/20-20/400). The most common diagnoses in FTM patients were neurologic (71.4 %), particularly idiopathic intracranial hypertension, while MTF transgenders presented mainly with chorioretinal diseases (40.0 %). Compliance with medical recommendations and follow-up appointments was seen in 71.4 % of FTM and 50.0 % of MTF patients. At the last visit, the median visual acuity was 20/50 (range; 20/20-20/70) in FTM and 20/25 (range; 20/20-20/70) in MTF patients. Conclusions and importanceTransgenders presented a variety of ocular findings. A cause-and-effect association cannot be stated, yet eye specialists must be cognizant of these findings to provide appropriate treatment.

Impact of Community Eye Clinics (CEC) on Specialist Eye Clinic Referrals

ABSTRACT Purpose Community Eye Clinics (CEC) increase accessibility of specialist ophthalmic services in the community, reducing demand for tertiary eye services. This paper aims to evaluate the impact of CEC on first-visit referrals from Hougang Polyclinic (HOU) to Tan Tock Seng Hospital Ophthalmology Specialist Outpatient Clinic (SOC). Methods A retrospective analysis was performed on first-visit referrals from Hougang Polyclinic (HOU) to Tan Tock Seng Hospital Ophthalmology Specialist Outpatient Clinic (SOC) over a similar 3-months period before and after the introduction of CEC in August 2018 (1 January to 31 March in 2018 and 2019, respectively). Data pertaining to patients’ presenting complaints, referral reasons, final diagnoses, follow-up plans, and need for ophthalmic intervention were obtained. Results We included 978 patients in our study. There was a 27.5% reduction in the number of first-visit referrals seen at SOC after the establishment of CEC. Patients were more likely to be referred on to sub-specialty eye clinics (10.8% vs. 12.9%, p= p = .304) and receive more ophthalmic interventions (15% vs. 16.3%, p = .066) than prior to CEC. Conclusion The CEC provides greater accessibility to eye care within the community. Optometrists are upskilled to manage patients with stable eye conditions, whilst eye specialists can provide timely care to the SOC for patients with more severe eye conditions.

Ocular manifestations in COVID-19 Patients at Dr. Wahidin Sudiro Husodo General Hospital Mojokerto during January – June 2021

Coronavirus Disease (COVID-19) is a respiratory infection caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). SARS-CoV-2 can be found in ocular organs through direct inoculation, migration via the nasolacrimal duct, or hematogenous. This study aims to determine the clinical manifestations of eye disease in COVID-19 patients at Dr. Wahidin Sudiro Husodo Mojokerto General Hospital from January-June 2021. A cross-sectional retrospective observational study by collecting secondary data on patient medical records. The research sample was all outpatients and inpatients with a positive PCR test for COVID-19 who were consulted by an ophthalmologist at Dr. Wahidin Sudiro Husodo General Hospital Mojokerto in January-June 2021. A total of 861 cases of patients with positive PCR tests for COVID-19, a total of 67 patients were consulted to eye specialists with a median age of 42.6 years. Among 67 patients, 59 (88%) showed symptoms of conjunctival injection, 8 patients (12%) ciliary infusion with 7 patients diagnosed with keratitis, and 1 patient with ulcer redness. In conclusion, the main eye manifestation most commonly found in COVID-19 patients is conjunctivitis which is characterized by conjunctival hyperemia.

Segmentation of paracentral acute middle maculopathy lesions in spectral-domain optical coherence tomography images through weakly supervised deep convolutional networks

Background and objectivesSpectral-domain optical coherence tomography (SD-OCT) is a valuable tool for non-invasive imaging of the retina, allowing the discovery and visualization of localized lesions, the presence of which is associated with eye diseases. The present study introduces X-Net, a weakly supervised deep-learning framework for automated segmentation of paracentral acute middle maculopathy (PAMM) lesions in retinal SD-OCT images. Despite recent advances in the development of automatic methods for clinical analysis of OCT scans, there remains a scarcity of studies focusing on the automated detection of small retinal focal lesions. Additionally, most existing solutions depend on supervised learning, which can be time-consuming and require extensive image labeling, whereas X-Net offers a solution to these challenges. As far as we can determine, no prior study has addressed the segmentation of PAMM lesions in SD-OCT images. MethodsThis study leverages 133 SD-OCT retinal images, each containing instances of paracentral acute middle maculopathy lesions. A team of eye experts annotated the PAMM lesions in these images using bounding boxes. Then, labeled data were used to train a U-Net that performs pre-segmentation, producing region labels of pixel-level accuracy. To attain a highly-accurate final segmentation, we introduced X-Net, a novel neural network made up of a master and a slave U-Net. During training, it takes the expert annotated, and pixel-level pre-segment annotated images and employs sophisticated strategies to ensure the highest segmentation accuracy. ResultsThe proposed method was rigorously evaluated on clinical retinal images excluded from training and achieved an accuracy of 99% with a high level of similarity between the automatic segmentation and expert annotation, as demonstrated by a mean Intersection-over-Union of 0.8. Alternative methods were tested on the same data. Single-stage neural networks proved insufficient for achieving satisfactory results, confirming that more advanced solutions, such as the proposed method, are necessary. We also found that X-Net using Attention U-net for both the pre-segmentation and X-Net arms for the final segmentation shows comparable performance to the proposed method, suggesting that the proposed approach remains a viable solution even when implemented with variants of the classic U-Net. ConclusionsThe proposed method exhibits reasonably high performance, validated through quantitative and qualitative evaluations. Medical eye specialists have also verified its validity and accuracy. Thus, it could be a viable tool in the clinical assessment of the retina. Additionally, the demonstrated approach for annotating the training set has proven to be effective in reducing the expert workload.

Integration on the Frontlines of Medicaid Accountable Care Organizations and Associations With Perceived Care Quality, Health Equity, and Satisfaction.

Amid enthusiasm about accountable care organizations (ACOs) in Medicaid, little is known about the primary care practices engaging in them. We leverage a survey of administrators within a random sample (stratified by ACO) of 225 practices joining Massachusetts Medicaid ACOs (64% response rate; 225 responses). We measure the integration of processes with distinct entities: consulting clinicians, eye specialists for diabetes care, mental/behavioral care providers, and long-term and social services agencies. Using multivariable regression, we examine organizational correlates of integration and assess integration's relationships with care quality improvement, health equity, and satisfaction with the ACO. Integration varied across practices. Clinical integration was positively associated with perceived care quality improvement; social service integration was positively associated with addressing equity; and mental/behavioral and long-term service integration were positively associated with ACO satisfaction (all p < .05). Understanding differences in integration at the practice level is vital for sharpening policy, setting expectations, and supporting improvement in Medicaid ACOs.

Retinal Fundus Multi-Disease Image Dataset (RFMiD) 2.0: A Dataset of Frequently and Rarely Identified Diseases

Irreversible vision loss is a worldwide threat. Developing a computer-aided diagnosis system to detect retinal fundus diseases is extremely useful and serviceable to ophthalmologists. Early detection, diagnosis, and correct treatment could save the eye’s vision. Nevertheless, an eye may be afflicted with several diseases if proper care is not taken. A single retinal fundus image might be linked to one or more diseases. Age-related macular degeneration, cataracts, diabetic retinopathy, Glaucoma, and uncorrected refractive errors are the leading causes of visual impairment. Our research team at the center of excellence lab has generated a new dataset called the Retinal Fundus Multi-Disease Image Dataset 2.0 (RFMiD2.0). This dataset includes around 860 retinal fundus images, annotated by three eye specialists, and is a multiclass, multilabel dataset. We gathered images from a research facility in Jalna and Nanded, where patients across Maharashtra come for preventative and therapeutic eye care. Our dataset would be the second publicly available dataset consisting of the most frequent diseases, along with some rarely identified diseases. This dataset is auxiliary to the previously published RFMiD dataset. This dataset would be significant for the research and development of artificial intelligence in ophthalmology.

Comparative study of protective coating properties of CR-39-based spectacle lenses

This research aimed to study and compare the mechanical durability and contamination resistance of organic spectacle lenses for everyday use, based on CR-39 poly(allyl diglycol carbonate) (PADC), from different manufacturers. The objects of this research are selected corrective lenses from three different manufacturers. Each examined brand is represented by a reference uncoated lens and three other lenses with different types of protective coating. Each sample has been characterized experimentally in our laboratory conditions according to three indicators of the surface: microhardness, roughness, and wetting properties. The results of this study are targeted to the eye specialists to compare the surface properties, essential for the daily usage of the spectacles, as well as the differences depending on the lens manufacturer.

Floppy iris syndrome associated with specific medication intake: A narrative review.

Intraoperative floppy iris syndrome (IFIS) is a cataract surgery complication that remains a challenge for eye surgeons. It is caused by the antagonism of alfa-1-adrenergic receptors within the dilator muscle of the iris, thus preventing the iris from dilation during a cataract surgery. The long-term blocking alfa-1 adrenergic receptors by the chronic use of a number of systemic medications may lead to permanent anatomical atrophy of the dilator muscle of the iris. The most common drugs associated with the development of IFIS are tamsulosin and other alpha-1 adrenergic receptor antagonists prescribed to patients with low urinary tract symptoms (LUTS). There are other systemic medications that have been reported to have increased risk for IFIS. It is crucial for the ophthalmologist to identify the high-risk patients prone to develop IFIS. Its presence may complicate the course of cataract surgery, ultimately negatively affecting visual outcome. Cataract surgery should be performed by an experienced eye surgeon using alternative pharmacological and surgical techniques. Interdisciplinary cooperation is essential to mitigate potential complications. Patients should be informed by their physicians about the need to report a medication history to their eye specialists, especially before cataract surgery.

Perception of ocular complications among patients with diabetes mellitus attending tertiary hospitals in Enugu, Nigeria

Background: Diabetes mellitus can lead to sight and life-threatening complications. It affects people in both developed and developing countries. Many diabetics present to the eye clinics with undetected advanced eye complications. Aim: The study aimed at evaluating the perception of the ocular complications of diabetes and how it influences the attitude and practice of the diabetics attending the eye clinics. Materials and Methods: A cross-sectional study was conducted in two tertiary hospitals in Enugu over a 3-month period. An interviewer-administered questionnaire was used to assess their perception, and hence their attitude and practice toward diabetic eye complications after obtaining their informed consents. Results: A total of 884 diabetics participated. There were 337 (38.1%) males and 547 (61.9%) females. A majority, 523 (59.2%), were in the age range of 51–70 years. Only 40.7% knew that diabetes affects the eyes; 60.2% did not know that good blood sugar control will help prevent eye complications; 41.1% knew they needed to see the eye specialist from time to time but not necessarily for routine check-up. Only 9.1% saw the eye specialists routinely; the remaining 90.9% never went for routine eye check as only 40.72 knew it affects the eyes. Conclusion: There was poor perception of the disease and its complications, which led to poor practice such as no regular, periodic eye examinations. There is a dire need for education of the diabetics, building of multidisciplinary effective strategies and policies with the view to decreasing the burden of the disease especially in the developing countries.

Suspected dementia, the eye and the ‘mind’s eye’: How primary care eye specialists could support early identification of Alzheimer’s Disease and its variants

The current article outlines the nature of visuoperceptual deficits in Alzheimer’s Disease (AD) and its ‘atypical’ variants. Evidence to highlight that visual and visuoperceptual difficulties are common in dementias driven by Alzheimer’s-based pathology will be discussed. Following this, the ways in which early identification of visual and lower-level visuoperceptual symptoms of dementia may be facilitated within primary care optical health settings will be considered. It is hoped that this may inspire work to support opticians and optometrists to identify where timely signposting to dementia services may be beneficial for people with suspected dementia who are experiencing visuoperceptual deficits. This is particularly important given that visual and visuoperceptual deficits can be early signs of dementia and often have a significant impact on activities of daily living (e.g. dressing, increased falls risk, accidents whilst driving, difficulties navigating familiar environments; Tippett et al., 2019). An illustrative case example is provided, outlining how timely detection of suspected dementia could have enabled early provision of appropriate treatment and support to enhance quality of life (e.g. medication for symptom management, provision of cognitive strategies and functional aids; Armstrong &amp; Kergoat, 2015).

CNN-Hyperparameter Optimization for Diabetic Maculopathy Diagnosis in Optical Coherence Tomography and Fundus Retinography

Diabetic Maculopathy (DM) is considered the most common cause of permanent visual impairment in diabetic patients. The absence of clear pathological symptoms of DM hinders the timely diagnosis and treatment of such a critical condition. Early diagnosis of DM is feasible through eye screening technologies. However, manual inspection of retinography images by eye specialists is a time-consuming routine. Therefore, many deep learning-based computer-aided diagnosis systems have been recently developed for the automatic prognosis of DM in retinal images. Manual tuning of deep learning network’s hyperparameters is a common practice in the literature. However, hyperparameter optimization has shown to be promising in improving the performance of deep learning networks in classifying several diseases. This study investigates the impact of using the Bayesian optimization (BO) algorithm on the classification performance of deep learning networks in detecting DM in retinal images. In this research, we propose two new custom Convolutional Neural Network (CNN) models to detect DM in two distinct types of retinal photography; Optical Coherence Tomography (OCT) and fundus retinography datasets. The Bayesian optimization approach is utilized to determine the optimal architectures of the proposed CNNs and optimize their hyperparameters. The findings of this study reveal the effectiveness of using the Bayesian optimization for fine-tuning the model hyperparameters in improving the performance of the proposed CNNs for the classification of diabetic maculopathy in fundus and OCT images. The pre-trained CNN models of AlexNet, VGG16Net, VGG 19Net, GoogleNet, and ResNet-50 are employed to be compared with the proposed CNN-based models. Statistical analyses, based on a one-way analysis of variance (ANOVA) test, receiver operating characteristic (ROC) curve, and histogram, are performed to confirm the performance of the proposed models.

Associations between age and types of presentation of refractive errors in children and young adults 0-30 years attending specialist referral hospital in Yemen: a cross-sectional study

Background: Refractive errors are the most common cause of visual impairment worldwide. Proportion of age group is varied among societies and they are considered as a public health challenge and common presentations in general practice in eye clinics. Understanding the associations between age and types of presentation of refractive errors in children and young adults 0-30 years, will aid early identification, diagnosis, referral and treatment. Aim: The aim of this study was to determine the associations between age and types of presentation of refractive errors in children and young adults, 0-30 years. Methodology: A cross-sectional study of 1,500 out-patients aged from 0–30 years attending ophthalmology clinic in Sanaa, Yemen (between 2012-2015) were included in the study. All patients underwent visual acuity examination, auto-refractometer, anterior and posterior segment examination, and were grouped according to age and type, i.e., myopia, hyperopia, and astigmatism. Odds ratios (OR) and 95% confidence intervals (95% CI) were calculated to evaluate the association between age and types of presentation of refractive errors. Results: Hyperopia was the most common single diagnosis (53.3%) followed by myopia (33.3%). Astigmatism was uncommon as a single diagnosis (13.4%) but commonly associated with hyperopia or myopia. Myopia was more common in males (42.9%), than females (25%). Hyperopia was more in females (62.5%) than males (42.9%). Age groups most affected by refractive errors were 13-18 years (27.7%), 19-24 years (24.8%) and 25-30 years (24.6%) respectively. Conclusions: This study highlights the close associations between age and types of presentation of refractive error. Early identification and proper categorization of refractive errors by age, gender, and other demographics by general physicians in primary care can better deduce and make useful referrals to eye specialists. Keywords: Association, age, myopia, hyperopia, astigmatism, Yemen

An Automated Image Segmentation and Useful Feature Extraction Algorithm for Retinal Blood Vessels in Fundus Images

The manual segmentation of the blood vessels in retinal images has numerous limitations. It is very time consuming and prone to human error, particularly with a very twisted structure of the blood vessel and a vast number of retinal images that needs to be analysed. Therefore, an automatic algorithm for segmenting and extracting useful clinical features from the retinal blood vessels is critical to help ophthalmologists and eye specialists to diagnose different retinal diseases and to assess early treatment. An accurate, rapid, and fully automatic blood vessel segmentation and clinical features measurement algorithm for retinal fundus images is proposed to improve the diagnosis precision and decrease the workload of the ophthalmologists. The main pipeline of the proposed algorithm is composed of two essential stages: image segmentation and clinical features extraction stage. Several comprehensive experiments were carried out to assess the performance of the developed fully automated segmentation algorithm in detecting the retinal blood vessels using two extremely challenging fundus images datasets, named the DRIVE and HRF. Initially, the accuracy of the proposed algorithm was evaluated in terms of adequately detecting the retinal blood vessels. In these experiments, five quantitative performances were measured and calculated to validate the efficiency of the proposed algorithm, which consist of the Acc., Sen., Spe., PPV, and NPV measures compared with current state-of-the-art vessel segmentation approaches on the DRIVE dataset. The results obtained showed a significantly improvement by achieving an Acc., Sen., Spe., PPV, and NPV of 99.55%, 99.93%, 99.09%, 93.45%, and 98.89, respectively.

Computer‐aided diagnosis of cataract severity using retinal fundus images and deep learning

AbstractCataracts are lenticular opacities that can appear in different parts of the lens in the eye and are a leading cause of blindness globally. Accurate detection and early‐stage diagnosis can prevent the cataract and improve the quality of life for cataract patients. However, clinical cataract detection and grading require the expertise of trained eye specialists, which may impede everyone's early intervention due to the underlying expenses. This article proposed a computer aid diagnosis method for cataract detection, which also grades the severity of cataracts from fundus retinal images such as normal, mild, moderate, and severe. The proposed method uses a hybrid approach in which various pre‐trained convolutional neural networks (AlexNet, VGGNet, ResNet) with transfer learning are used to extract features. These feature vectors of each network individually and in the fused form are applied on the support vector machine classifiers for 4‐stage cataract classification. This architecture also takes advantage of ensemble learning by applying a majority voting scheme on the predictions of these SVM classifiers. The fundus cataract images are obtained from several open‐access datasets and arranged into 4‐classes with the assistance of an eye specialist. Since all the collected images are not suitable for diagnosis, an image quality selection module is included with this method to determine the quality of fundus images. The proposed method achieved 96.25% 4‐class classification accuracy. According to the experimental results, the proposed method is effective for cataract classification and outperforms conventional methods.

An Update on Choroidal Layer Segmentation Methods in Optical Coherence Tomography Images: a Review.

Choroid is one of the structural layers, playing a significant role in physiology of the eye and lying between the sclera and the retina. The segmentation of this layer could guide ophthalmologists in diagnosing most of the eye pathologies such as choroidal tumors and polypoidal choroidal vasculopathy. High signal-to-noise ratio and high speed imaging in Spectral-Domain Optical Coherence Tomography (SD-OCT) make choroidal imaging feasible. Several variables such as pre-operative axial length (AXL), time of day and age affect thickness of the choroidal vascularization and should be considered for segmentation of this layer. These days most of the eye specialists manually segment the choroidal layer which is time-consuming, tiresome and dependent on human errors. To overcome these difficulties, some studies have introduced different automatic choroidal segmentation methods. In this paper, we have conducted a comprehensive review on existing recently published methods for automatic choroidal segmentation algorithms.

Deep Learning-Based Glaucoma Detection With Cropped Optic Cup and Disc and Blood Vessel Segmentation

Glaucoma is an irreversible neurodegenerative disease, where intraocular hypertension is developed due to the increased aqueous humor and blockage of the drainage system between the iris and cornea. As a result, the optic nerve head, which sends visual stimulus from our eyes to the brain, is damaged, causing visual field loss and ultimately blindness. Glaucoma is considered as the sneak thief of vision because it is difficult to diagnose early, and its regular screening is highly recommended to distinguish the neurological disorder. The detection of glaucoma is costly and time-consuming and not only there always remains a good possibility of human error but also this detection method is dependent upon the availability of the resources (experienced ophthalmologists and expensive instruments). In this work, an automatic glaucoma classification technique has been developed by utilizing multiple deep learning approaches. First, a new private dataset of 634 color fundus images has been collected and annotated by two eye specialists, a pediatric ophthalmologist and a glaucoma and refractive surgeon, from Bangladesh Eye Hospital, Bangladesh. Next, various deep learning models (EfficientNet, MobileNet, DenseNet, and GoogLeNet) have been used to detect glaucoma from fundus images. The model with EfficientNet-b3 architecture achieved the best results with test accuracy, F1-score, and ROC AUC of 0.9652, 0.9512, and 0.9574, respectively, for the cropped optic cup and disc fundus photographs. We also constructed a new dataset by segmenting the blood vessels from retinal fundus images employing a U-net model trained on High-Resolution Fundus Image Database. The MobileNet v3 model trained on this dataset achieved a satisfactory test accuracy of 0.8348 and an F1-score of 0.7957. This impressive result suggests that blood vessel segmentation of fundus images can be utilized as an alternative to detect glaucoma automatically.

Derivations of Vector Area and Volume Elements in Curved Coordinate Systems for Flux Vector Fields Helping Eye Disease

Our aim in this paper is to interest retinal eye specialists in preventing dry macula degeneration by a special flurry vector field through open or closed curved surfaces. The flux of vector fields through surfaces is based on vector element area and volume element. Therefore, we explain a few geometrical derivations of area and volume elements in curved orthogonal coordinate systems. We hope that by derivation of a spatial vector field flurry against drusen through open or closed surfaces due to the Gauss theorem might select drusen under eye retina cells without destroying the cells and prevent macula degeneration. A changed flurry of a magnetic or electric vector field through a closed line causes an electric or magnetic vector field on the surface closed by the line. We also hope that derivation by Stokes’ and Greens’ theorems, with the help of iron, might help eye cells to get in life.