Cataract Screening Research Articles

Development and Testing of Artificial Intelligence-Based Mobile Application to Achieve Cataract Backlog-Free Status in Uttar Pradesh, India

BackgroundUttar Pradesh (UP), the most populous state in India, has about 36 million people aged 50 years or older, spread across more than 100,000 villages. Among them, an estimated 3.5 million suffer from visual impairments, including blindness due to untreated cataracts. To achieve cataract backlog-free status, UP is required to screen this population at the community level and provide treatment to those suffering from cataracts. We envisioned an AI-powered primary screening app utilizing eye images, deployable to frontline health workers for community-level screening. This paper outlines insights gained from developing the AI mobile app “Roshni” for cataract screening. MethodThe AI-based cataract classification model was developed using 13,633 eye images and finalized after three stages of experiments, detecting cataracts in images focused on the eye, iris, and pupil. Overall, 155 experiments were conducted using multiple deep learning algorithms, including ResNet50, ResNet101, YOLOv5, EfficientNetV2, and InceptionV3. We established a minimum threshold of 90 % specificity and sensitivity to ensure the algorithm’s suitability for field use. ResultsThe cataract detection model for eye-focused images achieved 51.9 % sensitivity and 87.6 % specificity, while the model for iris-focused images, using a good/bad iris filter, achieved 52.4 % sensitivity and 93.3 % specificity. The classification model for segmented-pupil images, employing a good/bad pupil filter with UNet-based semantic segmentation model and EfficientNetV2, yielded 96 % sensitivity and 97 % specificity. Field testing with 302 beneficiaries (604 images) showed an overall sensitivity of 86.6 %, specificity of 93.3 %, positive predictive value of 58.4 %, and negative predictive value of 98.5 %. ConclusionThis paper details the development of an AI mobile app designed to facilitate community screening for cataracts by frontline health workers.

The global burden of cataracts and its attributable risk factors in 204 countries and territories: a systematic analysis of the global burden of disease study.

The global distribution and trends in the attributable burden of cataract risk have rarely been systematically explored. To guide the development of targeted and accurate cataract screening and treatment strategies, we analyzed the burden of cataract disease attributable to known risk factors. This study utilized detailed cataract data from the Global Burden of Disease e 2019, and we analyzed disability-adjusted life years (DALYs) e each risk factor from 1990 to 2019. Additionally, we calculated estimated annual percentage changes (EAPCs) during the study period. The results revealed that from 1990-2019, the global age-standardized DALYs of e attributable to particulate matter pollution, smoking, high fasting glucose plasma and high BMI showed steady downward trends (1990-2009: EAPC = -0.21 [-0.57 -0.14]); 2000-2009: EAPC = -0.95 [-1.01 -0.89]; 2010-2019: EAPC = -1.41 [-1.8 -1.02]). The age-standardized DALYs and mortality caused by each risk factor were highest in the low-middle sociodemographic index (SDI) region (EAPC = -1.77[(-2.19--1.34)]). The overall disease burden of cataracts is lower in males than in females. When analyzing the EAPCs of cataract disease burden for each risk factor individually, we found that the age-standardized disability-adjusted life years caused by particulate matter pollution and smoking decreased (PMP1990-2009: EAPC = -0.53 [-0.9--0.16]; 2000-2009: EAPC = -1.39 [-1.45--1.32]; 2010-2019: EAPC = -2.27 [-2.75--1.79]; smoking 2000 to 2009: EAPC = -1.51 [-1.6--1.43], 2009 to 2019: EAPC = -1.34 [-1.68--1])), while high fasting plasma glucose and high body mass index increased annually (HFPG1990 to 1999: EAPC = 1.27 [0.89-1.65], 2000 to 2009: EAPC = 1.02 [0.82-1.22], 2010-2019: EAPC = 0.44 [0.19-0.68]; HBMI 1990 to 1999: EAPC = 1.65 [1.37-1.94], 2000 to 2009: EAPC = 1.56 [1.43-1.68], 2010-2019: EAPC = 1.47 [1.18-1.77]). The burden of cataracts caused by ambient particulate matter and smoking is increasing in low, low-middle SDI areas, and specific and effective measures are urgently needed. The results of this study suggest that reducing particulate matter pollution, quitting smoking, controlling blood glucose, and lowering BMI could play important roles in reducing the occurrence of cataracts, especially in older people.

Innovative utilization of ultra-wide field fundus images and deep learning algorithms for screening high-risk posterior polar cataract.

To test a cataract shadow projection theory and validate it by developing a deep learning algorithm that enables automatic and stable posterior polar cataract (PPC) screening using fundus images. Department of Ophthalmology, Far Eastern Memorial Hospital, New Taipei, Taiwan. Retrospective chart review. A deep learning algorithm to automatically detect PPC was developed based on the cataract shadow projection theory. Retrospective data (n = 546) with ultra-wide field fundus images were collected, and various model architectures and fields of view were tested for optimization. The final model achieved 80% overall accuracy, with 88.2% sensitivity and 93.4% specificity in PPC screening on a clinical validation dataset (n = 103). This study established a significant relationship between PPC and the projected shadow, which may help surgeons to identify potential PPC risks preoperatively and reduce the incidence of posterior capsular rupture during cataract surgery.

Cost-effectiveness and cost-utility of a digital technology-driven hierarchical healthcare screening pattern in China

Utilization of digital technologies for cataract screening in primary care is a potential solution for addressing the dilemma between the growing aging population and unequally distributed resources. Here, we propose a digital technology-driven hierarchical screening (DH screening) pattern implemented in China to promote the equity and accessibility of healthcare. It consists of home-based mobile artificial intelligence (AI) screening, community-based AI diagnosis, and referral to hospitals. We utilize decision-analytic Markov models to evaluate the cost-effectiveness and cost-utility of different cataract screening strategies (no screening, telescreening, AI screening and DH screening). A simulated cohort of 100,000 individuals from age 50 is built through a total of 30 1-year Markov cycles. The primary outcomes are incremental cost-effectiveness ratio and incremental cost-utility ratio. The results show that DH screening dominates no screening, telescreening and AI screening in urban and rural China. Annual DH screening emerges as the most economically effective strategy with 341 (338 to 344) and 1326 (1312 to 1340) years of blindness avoided compared with telescreening, and 37 (35 to 39) and 140 (131 to 148) years compared with AI screening in urban and rural settings, respectively. The findings remain robust across all sensitivity analyses conducted. Here, we report that DH screening is cost-effective in urban and rural China, and the annual screening proves to be the most cost-effective option, providing an economic rationale for policymakers promoting public eye health in low- and middle-income countries.

CATARACT SYMPTOMS SCALE AS A SCREENING TOOL FOR VISION DISORDERS DUE TO CATARACT BY NON-MEDICAL CADRES

Introduction: Cataract is an eye disease with clouding of the lens causing visual interference. Blindness due to cataracts is still the leading cause of blindness in the world. Cataract screening using the questionnaire integrated interview method can be an easy instrument for non-medical cadres to use. Cataract Symptoms Scale (CSS) is a complaint-based questionnaire in cataract patients which is used to screen for visual disturbances due to cataracts.
 Objective: To find out whether CSS has good validity as a screening tool for visual impairment due to cataracts by non-medical cadres.
 Methods: This study used a diagnostic test to assess the accuracy of CSS. All respondents who met the inclusion and exclusion criteria were included as research subjects.
 Results: 151 respondents included in the vision screening activity of the Community Ophthalmology Unit of Cicendo Eye Hospital. Respondents with cataracts in this study amounted to 59 (39.07%) and non-cataracts amounted to 92 (40.93%). The most complaints of respondents were blurry with 145 complaints and the least complaints were changes in the color of objects, namely 36 complaints. The CSS questionnaire sensitivity value of 61.01% indicates a weak value statistically. The specificity value of 78.26% indicates a moderate specificity value statistically. The positive predictive value of 64.28% indicates a statistically weak NDP value. The negative predictive value of 75.78% indicates a statistically moderate value. The value of accuracy is 71.52% statistically moderate.
 Conclusions: The Cataract Symptoms Scale has poor validity to be used by non-medical cadres as a screening tool for patients with visual impairment due to cataracts. Further research is needed by adjusting the components of the questionnaire and adding variables such as visual acuity and cataract laterality.

SCREENING ON CATARACT APPLICATION (SERIOC)

Introduction & Objectives : West Nusa Tenggara is a province that has the second highest prevalence of blindness at the national level according to the results of the 2014 Rapid Assessment of Avoidable Blindness (RAAB) survey. During the covid-19 period the offline cataract screening is not permitted, as a result it’s difficult to reach underprivileged and isolated areas, also people with disabilities. In 2021 West Nusa Tenggara Eye Hospital launched a smartphone-based eye health examination application Screening On Cataract namely SERIOC, while still paying attention to service standards clinically and in management. SERIOC can be used by public health as the nearest public health facility.
 Methods : The waterfall method is used in the development of this application, The waterfall methodology is a project management approach that emphasizes a linear progression from beginning to end of a project. This methodology, is front-loaded to rely on careful planning, detailed documentation, and consecutive execution.
 Results : SERIOC has successfully screened 2,713 patients from 2021 with 2117 patients diagnosed cataract and 593 patients diagnosed non-cataract whose data is centralized and directly connected to an ophthalmologist at the West Nusa Tenggara Eye Hospital. Patients diagnosed with cataracts will be referred to the hospital for further action.
 Conclusion : Based on the information above, it can be concluded that SERIOC application in eye health screening is very effective and efficient method to avoid blindness on West Nusa Tenggara.

The Slitscope.

Slit lamp biomicroscope is the right hand of an Ophthalmologist. Even though precise, its bulky design and complex working process are limiting constraints, making it difficult for screening at outreach camps, which are an integral part of this field for the purpose of eliminating needless blindness. The torchlight is the main tool used for screening. Recently, the integration of smartphones with instruments and the digitization of slit lamp has been explored, to provide simple and easy hacks. By bringing the slit of the slit lamp to traditional torchlight, we have created "The Slitscope". It combines the best of both worlds as a simple innovative do-it-yourself novel technique for precise cataract screening. It is especially useful in peripheral centers, vision centers, and outreach camps. We present two prototypes which can also be 3D printed.

Clinical validation of artificial intelligence-based cataract screening solution with smartphone images (Logy AI cataract screening module)

Background: Purpose of the study was to clinically assess the accuracy of Logy AI cataract screening solution, an artificial intelligence-based module, which works through WhatsApp and also as a separate smart phone application, that can detect cataracts using images taken by a smartphone camera, by comparing with slit lamp based diagnoses made by ophthalmologists. Methods: A prospective clinical study was conducted in an eye clinic of a tertiary care hospital in the southern part of India with 437 patients. Smartphone images taken were sent to the Logy AI cataract screening solution which predicted if the patient had cataract or not. It graded cataracts as immature and mature. Patients were examined by ophthalmologists with slit-lamp and diagnosis was documented. Both were compared. Results: 794 eye images were included in the study. The overall accuracy of the AI screening solution for cataract detection was computed to be 90.08%. Further, the accuracy was 88.02% for immature cataract, 97.16% for mature cataract, and 90.08% normal category. The sensitivity was 90.38%, the specificity was 89.87%, and the F1 score was 87.98%. The positive predictive value was 85.71% and the negative predictive value was 93.29%. Logy AI cataract prediction module’s AUC (0.8946) falls under the good category. Conclusions: Logy AI cataract screening module could work as an effective cataract screening tool at the community level in remote areas where there is no expensive equipment and ophthalmic health care workers considering the accuracy and efficiency to work in low resource settings. It can also be a good home screening tool suitable for the post-COVID era.

Applications of Artificial Intelligence in Cataract Surgery: A Review.

Cataract surgery is one of the most performed procedures worldwide, and cataracts are rising in prevalence in our aging population. With the increasing utilization of artificial intelligence (AI) in the medical field, we aimed to understand the extent of present AI applications in ophthalmic microsurgery, specifically cataract surgery. We conducted a literature search on PubMed and Google Scholar using keywords related to the application of AI in cataract surgery and included relevant articles published since 2010 in our review. The literature search yielded information on AI mechanisms such as machine learning (ML), deep learning (DL), and convolutional neural networks (CNN) as they are being incorporated into pre-operative, intraoperative, and post-operative stages of cataract surgery. AI is currently integrated in the pre-operative stage of cataract surgery to calculate intraocular lens (IOL) power and diagnose cataracts with slit-lamp microscopy and retinal imaging. During the intraoperative stage, AI has been applied to risk calculation, tracking surgical workflow, multimodal imaging data analysis, and instrument location via the use of "smart instruments". AI is also involved in predicting post-operative complications, such as posterior capsular opacification and intraocular lens dislocation, and organizing follow-up patient care. Challenges such as limited imaging dataset availability, unstandardized deep learning analysis metrics, and lack of generalizability to novel datasets currently present obstacles to the enhanced application of AI in cataract surgery. Upon addressing these barriers in upcoming research, AI stands to improve cataract screening accessibility, junior physician training, and identification of surgical complications through future applications of AI in cataract surgery.

A deep network DeepOpacityNet for detection of cataracts from color fundus photographs

BackgroundCataract diagnosis typically requires in-person evaluation by an ophthalmologist. However, color fundus photography (CFP) is widely performed outside ophthalmology clinics, which could be exploited to increase the accessibility of cataract screening by automated detection.MethodsDeepOpacityNet was developed to detect cataracts from CFP and highlight the most relevant CFP features associated with cataracts. We used 17,514 CFPs from 2573 AREDS2 participants curated from the Age-Related Eye Diseases Study 2 (AREDS2) dataset, of which 8681 CFPs were labeled with cataracts. The ground truth labels were transferred from slit-lamp examination of nuclear cataracts and reading center grading of anterior segment photographs for cortical and posterior subcapsular cataracts. DeepOpacityNet was internally validated on an independent test set (20%), compared to three ophthalmologists on a subset of the test set (100 CFPs), externally validated on three datasets obtained from the Singapore Epidemiology of Eye Diseases study (SEED), and visualized to highlight important features.ResultsInternally, DeepOpacityNet achieved a superior accuracy of 0.66 (95% confidence interval (CI): 0.64–0.68) and an area under the curve (AUC) of 0.72 (95% CI: 0.70–0.74), compared to that of other state-of-the-art methods. DeepOpacityNet achieved an accuracy of 0.75, compared to an accuracy of 0.67 for the ophthalmologist with the highest performance. Externally, DeepOpacityNet achieved AUC scores of 0.86, 0.88, and 0.89 on SEED datasets, demonstrating the generalizability of our proposed method. Visualizations show that the visibility of blood vessels could be characteristic of cataract absence while blurred regions could be characteristic of cataract presence.ConclusionsDeepOpacityNet could detect cataracts from CFPs in AREDS2 with performance superior to that of ophthalmologists and generate interpretable results. The code and models are available at https://github.com/ncbi/DeepOpacityNet (https://doi.org/10.5281/zenodo.10127002).

Incorporating medical domain knowledge into data-driven method: A vessel attention guided multi-granularity network for automatic cataract classification

Early cataract screening is essential to help patients prevent irreversible vision loss. Existing data-driven methods can automatically extract deep fundus features for automatic cataract classification. However, due to the lack of medical domain knowledge, these deep features may contain large pathological irrelevant information, thus reducing the classification accuracy of data-driven methods. To fully take the advantages of domain knowledge to enhance the discriminative representation and avoid the irrelevant impact, we comprehensively investigate how to incorporate it into data-driven models. Considering the vessel information and multi-granularity representation are two key points of domain knowledge for cataract diagnosis, we propose a vessel attention guided multi-granularity network (VAM-Net), which contains three main components. Specifically, the vessel-level segmentation subnet is firstly designed by leveraging U-shape segmentation network. The refined vessel information obtained in this step can be regarded as visual attention to enable deep network extract discriminative features. With the guidance of vessel attention, the global-local classification subnet is further proposed to eliminate the interference of irrelevant information and enhance the multi-granularity feature learning (including global structural features and local subtle features). Finally, the multiple levels predictions are integrated into the final cataract diagnosis decision via constructing multi-granularity ensemble network. In this manner, both domain knowledge and deep networks are organically combined for pursuing better diagnosis performance. By setting extensive comparative and ablation experiments, we evaluate the effectiveness of the proposed VAM-Net on the real-world cataract dataset (92.78% detection accuracy and 87.68% grading accuracy).

A Systematic Review of Digital Ophthalmoscopes in Medicine.

Recent advances in telemedicine have led to increased use of digital ophthalmoscopes (DO) in clinical settings. This review aims to assess commercially available DOs, including smartphone (SP), desktop, and handheld ophthalmoscopes, and evaluate their applications. A literature review was performed by searching PubMed (pubmed.ncbi.nlm.nih.gov), Web of Science (webofknowledge.com), and Science Direct (sciencedirect.com). All English-language papers that resulted from the search terms "digital ophthalmoscope", "screening tool", "glaucoma screening", "diabetic retinopathy screening", "cataract screening", and "papilledema screening" were reviewed. Studies that contained randomized clinical trials with human participants between January 2010 and December 2020 were included. The Risk of Bias in Systematic Reviews (ROBIS) tool was used to assess the methodological quality of each included paper. Of the 1307 studies identified, 35 met inclusion and exclusion criteria. The ROBIS tool determined that 29/35 studies (82.8%) had a low risk of bias, 3/35 (8.5%) had a moderate risk of bias, and 3/35 (8.5%) had a high risk of bias. The continued adoption of DOs remains uncertain because of concerns about the image quality for non-mydriatic eyes and the confidence in data captured from the device. Likewise, there is a lack of guidelines for the use of DOs, which makes it difficult for providers to determine the best device for their practice and to ensure appropriate use. Even so, DOs continue to gain acceptance as technology and practice integration improve, especially in underserved areas with limited access to ophthalmologists.

VisualAcuity Threshold for CataractsSurgeryat a Tertiary Eye Center in Iraq

Purpose: To find out threshold of visual acuity for cataract surgery at a tertiary eye center of Iraq. Study Design: Descriptive observational. Place & Duration of Study: Ibn Al-Haitham Teaching Eye Hospital during the year 2022. Methods: It was a retrospective descriptive study in which 6650 patients who complained of visual impairment due to cataract, 30 to 80 years of age and either gender were included.Any intraocular pathology that leads to visual impairment other than cataract and congenital cataract were excluded. The best corrected visual acuity (BCVA) and the state of the fellow eye was also determined. Visual impairment was defined as mild; 6/9 - 6/18, moderate; 6/24 - 6/36 and severe visual impairment; 6/60 or less. Results: Data collected from 6650 cases showed 2625 (39.5%) males and 4025 (60.5%) females. Mild visual impairment was found in 875 (13.15%) patients, moderate was found in 175(2.63%) patients, and severe visual impairment was the most common type in 5250 (78.95%) patients. Conclusion: Most of the patients with cataract present late with severe impairment of visual acuity which requires national screening program with extra focuson risk groups. Nonetheless, these results are of potential use to planners of cataract screening outreach programs. It could be regarded as a preliminary study for further larger study on a national level to establish a scheme for patients’ diagnosis and referral to cataract surgery to get early diagnosis and shorten the waiting lists.

Advancements in Cataract Detection: The Systematic Development of LeNet-Convolutional Neural Network Models.

Regular screening and timely treatment play a crucial role in addressing the progression and visual impairment caused by cataracts, the leading cause of blindness in Thailand and many other countries. Despite the potential for prevention and successful treatment, patients often delay seeking medical attention due to the gradual and relatively asymptomatic nature of cataracts. To address this challenge, this research focuses on the identification of cataract abnormalities using image processing techniques and machine learning for preliminary assessment. The LeNet-convolutional neural network (LeNet-CNN) model is employed to train a dataset of digital camera images, and its performance is compared to the support vector machine (SVM) model in categorizing cataract abnormalities. The evaluation demonstrates that the LeNet-CNN model achieves impressive results in the testing phase. It attains an accuracy rate of 96%, exhibiting a sensitivity of 95% for detecting positive cases and a specificity of 96% for accurately identifying negative cases. These outcomes surpass the performance of previous studies in this field. This highlights the accuracy and effectiveness of the proposed approach, particularly the superior performance of LeNet-CNN. By utilizing image processing technology and convolutional neural networks, this research provides an effective tool for initial cataract screening. Patients can independently assess their eye health by capturing self-images, facilitating early intervention and medical consultations. The proposed method holds promise in enhancing the preliminary assessment of cataracts, enabling early detection and timely access to appropriate care.

Association of Behavioral and Clinical Risk Factors With Cataract: A Two-Sample Mendelian Randomization Study.

To investigate the association of genetically determined primary open-angle glaucoma (POAG), myopic refractive error (RE), type 2 diabetes (T2D), blood pressure (BP), body mass index (BMI), cigarette smoking, and alcohol consumption with the risk of age-related cataract. To assess potential causal effects of clinical or behavioral factors on cataract risk, we conducted two-sample Mendelian randomization analyses. Genetic instruments, based on common genetic variants associated with risk factors at genome-wide significance (P < 5 × 10-8), were derived from published genome-wide association studies (GWAS). For age-related cataract, we used GWAS summary statistics from our previous GWAS conducted in the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort (28,092 cataract cases and 50,487 controls; all non-Hispanic whites) or in the UK Biobank (31,852 cataract cases and 428,084 controls; all European-descent individuals). We used the inverse-variance weighted (IVW) method as our primary source of Mendelian randomization estimates and conducted common sensitivity analyses. We found that genetically determined POAG and mean spherical equivalent RE were significantly associated with cataract risk (IVW model: odds ratio [OR] = 1.04; 95% confidence interval [CI], 1.01-1.08; P = 0.018; per diopter more hyperopic: OR = 0.92; 95% CI, 0.89-0.93; P = 6.51 × 10-13, respectively). In contrast, genetically determined T2D, BP, BMI, cigarette smoking, or alcohol consumption were not associated with cataract risk (P > 0.05). Our results provide evidence that genetic risks for POAG and myopia may be causal risk factors for age-related cataract. These results are consistent with previous observational studies reporting associations of myopia with cataract risk. This information may support population cataract risk stratification and screening strategies.

Can the diagnostic accuracy of newborn eye screening for congenital cataract be improved with digital imaging? The Digital Imaging versus Ophthalmoscopy (DIvO) study

Background Congenital cataract is the commonest cause of preventable child blindness in the world. It affects 1: 3000 babies, 60% of whom will have bilateral disease. Although screening of the newborn red-reflex with an ophthalmoscope is recommended, there are questions over this technique's accuracy, particularly in non-specialist hands. Several studies in enriched paediatric eye clinic populations have demonstrated superior accuracy when a digital image of the the eye's reflection to infrared light is evaluated. Aim This study uses a prototype infrared digital camera to test the hypothesis that the sensitivity and specificity of newborn screening using evaluation of the "infrared-reflex" image is superior to the standard red-reflex examination using ophthalmoscopy. Methods 140,000 newborn babies will be recruited into the study from at least 13 maternity units in England over an 18 month period. Babies will have both the standard red-reflex assessment and evaluation of the infrared-reflex using a prototype device. Since specialist gold-standard evaluation of every participant is impractical, bespoke data linkage requests to NHS England will be made. Data from the red-reflex evaluation and the presence of codes relating to cataract diagnosis and/or treatment in Hospital Episode Statistics (HES) will be retrieved for each participant a minimum of 6 months after their birth. This data will be used to calculate relative and absolute sensitivity and specificity for each screening test and comparison of accuracy using the McNemar test. Secondary outcome measures will include comparison of accuracy in different ethnic groups and screener usability scores. Anticipated impact Confirmation of the hypothesis will support development of a commercial screening device and the possible revision of newborn screening recommendations. Trial registration The study is registered with clinicaltrials.gov: NCT05282147.

Cataracts in Havanese: genome wide association study reveals two loci associated with posterior polar cataract

BackgroundCataract is considered an important health issue in Havanese, and studies indicate a breed predisposition. Possible consequences of cataracts include lens induced uveitis, reduced eyesight, and blindness in severe cases. Reducing the prevalence of cataracts could therefore improve health and welfare significantly. The most frequently diagnosed forms of cataract in Havanese are cortical- and anterior suture line cataract, but cases of posterior polar cataract are also regularly reported. Out of the three, posterior polar- and cortical cataracts are considered the most clinically relevant.ResultsWe performed a genome wide association study that included 57 controls and 27 + 23 + 7 cases of cortical-, anterior suture line- and posterior polar cataract, respectively. An association analysis using a mixed linear model, revealed two SNPs on CFA20 (BICF2S23632983, p = 7.2e-09) and CFA21 (BICF2G630640490, p = 3.3e-09), that were significantly associated with posterior polar cataract, both of which are linked to relevant candidate genes. The results suggest that the two variants are linked to alleles with large effects on posterior polar cataract formation, possibly in a dominant fashion, and identifies regions that should be subject to further sequencing.Promising regions on CFA4 and CF30 were also identified in the association analysis of cortical cataract. The top SNPs on each chromosome, chr4_12164500 (p = 4.3e-06) and chr30_28836339 (p = 5.6e-06), are located within, or in immediate proximity to, potential cataract candidate genes.The study shows that age at examination is strongly associated with sensitivity of cataract screening. Havanese in Norway are on average 3.4 years old when eye examinations are performed: an age where most dogs that are genetically at risk have not yet developed clinically observable changes. Increasing the average age of breeding animals could increase accuracy of selection, leading to improved health.ConclusionsThe study identified two loci, on CFA20 and CFA21, that were significantly associated with posterior polar cataract in Havanese. SNPs that showed putative association with cortical cataracts, were observed on CFA4 and CFA30. All the top SNPs are located in close proximity to cataract candidate genes. The study also show that sensitivity of cataract screening is highly dependent on age at examination.

Effect of mydriatic eye drops (combined 0.8% tropicamide and 5% phenylephrine) on the measurements of corneal parameters using Pentacam.

The purpose of the study is to investigate the effects of combined 0.8% tropicamide and 5% phenylephrine on the corneal parameters using Pentacam. The study was performed on 200 eyes of 100 adult patients visiting the ophthalmology clinic for evaluation of refractive errors or cataract screening. Mydriatic drops (Tropifirin; Java, India) containing tropicamide 0.8%, phenylephrine hydrochloride 5%, and chlorbutol 0.5% (as a preservative) were instilled into the eyes of the patients three times every 10 minutes. The Pentacam was repeated after 30 minutes. The measurement data of various corneal parameters from different Pentacam displays (keratometry, pachymetry, densitometry, and Zernike analysis) was manually compiled on an Excel spreadsheet and analyzed using Statistical Package for the Social Sciences (SPSS) 20 software. Analysis of Pentacam refractive maps revealed a statistically significant increase (P < 0.05) in the values of radius peripheral (cornea front), pupil center Pachymetry, pachymetry apex, thinnest location Pachymetry, and cornea volume. However, pupil dilation did not affect the Q-value (asphericity). Analysis of the densitometry values revealed significant increase in all zones. Aberrations maps revealed statistically significant increase in the value of spherical aberration after the induction of mydriasis, but the values of Trefoil 0º, Trefoil 30º, Koma 90º, and Koma 0º were not affected significantly. We did not observe any untoward effect of the drug, except transient blurring of vision. The current study showed that routine mydriasis in the eye clinics leads to a significant increase in various corneal parameters including corneal pachymetry, cornea densitometry, and spherical aberration as measured by Pentacam, which can influence the decision-making in the management of various corneal diseases. The ophthalmologists should be aware of these issues and make adjustments in their surgical planning accordingly.

Cataract services for all: Strategies for equitable access from a global modified Delphi process.

Vision loss from cataract is unequally distributed, and there is very little evidence on how to overcome this inequity. This project aimed to engage multiple stakeholder groups to identify and prioritise (1) delivery strategies that improve access to cataract services for under-served groups and (2) population groups to target with these strategies across world regions. We recruited panellists knowledgeable about cataract services from eight world regions to complete a two-round online modified Delphi process. In Round 1, panellists answered open-ended questions about strategies to improve access to screening and surgery for cataract, and which population groups to target with these strategies. In Round 2, panellists ranked the strategies and groups to arrive at the final lists regionally and globally. 183 people completed both rounds (46% women). In total, 22 distinct population groups were identified. At the global level the priority groups for improving access to cataract services were people in rural/remote areas, with low socioeconomic status and low social support. South Asia and Sub-Saharan Africa were the only regions in which panellists ranked women in the top 5 priority groups. Panellists identified 16 and 19 discreet strategies to improve access to screening and surgical services, respectively. These mostly addressed health system/supply side factors, including policy, human resources, financing and service delivery. We believe these results can serve eye health decision-makers, researchers and funders as a starting point for coordinated action to improve access to cataract services, particularly among population groups who have historically been left behind.

Разработка модели скрининга катаракты с использованием открытого набора данных и алгоритмов глубокого машинного обучения

Relevance. Untreated cataract is the cause of permanent blindness. The main factors of untimely surgical treatment are the lack of patient's awareness about the need for surgical treatment (36.1%) and work or household employment (25.3%). Thus, regular cataract screening is an effective way to prevent blindness and identify patients in need of surgery. Purpose. Development of a cataract screening system based on an open data set, as well as its validation on clinical data. Material and methods. An open dataset (No. 1) of 9668 smartphone camera images, of which 4514 were cataracts and 5154 were normal eyes. The set for external validation (No. 2) was obtained under clinical conditions in the diagnostic department of the Krasnodar branch of the The S. Fyodorov Eye Microsurgery Federal State Institution. The set contained 51 cataract and normal images. To create a machine learning model, we used a convolutional neural network (CNN). Results. The data classification accuracy value was 0.97 for the internal validation set and 0.75 for the external one. The predictive value was low for cataract at the change in data set №2 and was only 0.54, as well as for sensitivity (0.87) and specificity (0.69) metrics. The area under the ROC curve was 0.99 (for dataset No. 1) and 0.78 (for dataset No. 2). Conclusion. These results indicate that it is necessary to fine-tune the model and provide the necessary levels of performance metrics for this scenario. Keywords: cataract, artificial intelligence, machine learning, screening, open datasets