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Aim:To evaluate the reliability of lens density measurement with anterior segment optical coherence tomography (OCT) and its association with the Lens Opacity Classification System Version III (LOCS III) grading.Methods:Fifty-five eyes from 55 age-related cataract patients were included. One eye from each subject was selected at random for lens evaluation. After dilation, lens photographs were taken with a slit lamp and graded against the LOCS III standardised condition. Anterior segment OCT imaging was performed on the same eyes with a high-resolution scan. The association between the anterior segment OCT nucleus density measurement and LOCS III nuclear opalescence (NO) and nuclear colour (NC) scores was evaluated with the Spearman correlation coefficient. Anterior segment OCT measurement precision, coefficient of variation (CVw), and intraclass correlation coefficient (ICC) were calculated.Results:The mean NO and NC scores were 3.39 (SD 1.10) and 3.37 (SD 1.27), respectively. Significant correlations were found between anterior segment OCT nuclear density measurements and the LOCS III NO and NC scores (r = 0.77 and 0.60, respectively, both with p<0.001). The precision, CVw and ICC of anterior segment OCT measurement were 2.05 units, 4.55% and 0.98, respectively.Conclusion:Anterior segment OCT nucleus density measurement is reliable and correlates with the LOCS III NO and NC scores.

Background We compared the Landolt C chart checked under normal clinical conditions and the Early Treatment of Diabetic Retinopathy Study (ETDRS) chart, using standard clinical research protocols for subjects with normal vision, cataract and maculopathy. Methods This prospective, comparative study was approved by the hospital Institutional Review Board. Patients with cataract and maculopathy were included, with the normal fellow eyes analyzed as normal vision group. Differences between the two charts were analyzed using Student’s t- test. Results Normal and cataract eyes showed no statistically significant differences between methods. In the maculopathy group, ETDRS acuity (0.714 ± 0.393) was better than Landolt C acuity (0.845 ± 0.579), but the differences were not statistically significant ( p  = 0.152). Furthermore, if after dividing visual acuity into subgroups, >20/200 and ≤20/200 by Landolt C acuity, the latter subgroup had significant differences between the two tests ( p  < 0.001). ETDRS acuity (1.014 ± 0.319) was better than Landolt C acuity (1.419 ± 0.385). The average acuity difference was 4 lines. Conclusions For maculopathy patients with VA ≤ 20/200, the ETDRS chart had a better score than the Landolt C chart.

PurposeTo establish and validate a universal artificial intelligence (AI) platform for collaborative management of cataracts involving multilevel clinical scenarios and explored an AI-based medical referral pattern to improve collaborative efficiency and resource coverage.MethodsThe training and validation datasets were derived from the Chinese Medical Alliance for Artificial Intelligence, covering multilevel healthcare facilities and capture modes. The datasets were labelled using a three-step strategy: (1) capture mode recognition; (2) cataract diagnosis as a normal lens, cataract or a postoperative eye and (3) detection of referable cataracts with respect to aetiology and severity. Moreover, we integrated the cataract AI agent with a real-world multilevel referral pattern involving self-monitoring at home, primary healthcare and specialised hospital services.ResultsThe universal AI platform and multilevel collaborative pattern showed robust diagnostic performance in three-step tasks: (1) capture mode recognition (area under the curve (AUC) 99.28%–99.71%), (2) cataract diagnosis (normal lens, cataract or postoperative eye with AUCs of 99.82%, 99.96% and 99.93% for mydriatic-slit lamp mode and AUCs >99% for other capture modes) and (3) detection of referable cataracts (AUCs >91% in all tests). In the real-world tertiary referral pattern, the agent suggested 30.3% of people be ‘referred’, substantially increasing the ophthalmologist-to-population service ratio by 10.2-fold compared with the traditional pattern.ConclusionsThe universal AI platform and multilevel collaborative pattern showed robust diagnostic performance and effective service for cataracts. The context of our AI-based medical referral pattern will be extended to other common disease conditions and resource-intensive situations.

To propose a new objective scatter index (OSI) based in the analysis of double-pass images of a point source to rank and classify cataract patients. This classification scheme is compared with a current subjective system. We selected a population including a group of normal young eyes as control and patients diagnosed with cataract (grades NO2, NO3 and NO4) according to the Lens Opacities Classification System (LOCS III). For each eye, we recorded double-pass retinal images of a point source. In each patient, we determined an objective scatter index (OSI) as the ratio of the intensity at an eccentric location in the image and the central part. This index provides information on the relevant forward scatter affecting vision. Since the double-pass retinal images are affected by both ocular aberrations and intraocular scattering, an analysis was performed to show the ranges of contributions of aberrations to the OSI. We used the OSI values to classify each eye according to the degree of scatter. The young normal eyes of the control group had OSI values below 1, while the OSI for subjects in LOCS grade II were around 1 to 2. The use of the objective index showed some of the weakness of subjective classification schemes. In particular, several subjects initially classified independently as grade NO2 or NO3 had similar OSI values, and in some cases even higher than subjects classified as grade NO4. A new classification scheme based in OSI is proposed. We introduced an objective index based in the analysis of double-pass retinal images to classify cataract patients. The method is robust and fully based in objective measurements; i.e., not depending on subjective decisions. This procedure could be used in combination with standard current methods to improve cataract patient surgery scheduling.

CorneAI for iOS is an artificial intelligence (AI) application to classify the condition of the cornea and cataract into nine categories: normal, infectious keratitis, non-infection keratitis, scar, tumor, deposit, acute primary angle closure, lens opacity, and bullous keratopathy. We evaluated its performance to classify multiple conditions of the cornea and cataract of various races in images published in the Cornea journal. The positive predictive value (PPV) of the top classification with the highest predictive score was 0.75, and the PPV for the top three classifications exceeded 0.80. For individual diseases, the highest PPVs were 0.91, 0.73, 0.42, 0.72, 0.77, and 0.55 for infectious keratitis, normal, non-infection keratitis, scar, tumor, and deposit, respectively. CorneAI for iOS achieved an area under the receiver operating characteristic curve of 0.78 (95% confidence interval [CI] 0.5–1.0) for normal, 0.76 (95% CI 0.67–0.85) for infectious keratitis, 0.81 (95% CI 0.64–0.97) for non-infection keratitis, 0.55 (95% CI 0.41–0.69) for scar, 0.62 (95% CI 0.27–0.97) for tumor, and 0.71 (95% CI 0.53–0.89) for deposit. CorneAI performed well in classifying various conditions of the cornea and cataract when used to diagnose journal images, including those with variable imaging conditions, ethnicities, and rare cases.

Background and Objectives: Diabetes has become a global epidemic, contributing to significant health challenges due to its complications. Among these, diabetes can affect sight through various mechanisms, emphasizing the importance of early identification and management of vision-threatening conditions in diabetic patients. Changes in the crystalline lens caused by diabetes may lead to temporary and permanent visual impairment. Since individuals with diabetes are at an increased risk of developing cataracts, which significantly affects their quality of life, this study aims to identify the most common cataract subtypes in diabetic patients, highlighting the need for proactive screening and early intervention. Materials and Methods: This study included 201 participants with cataracts (47.6% women and 52.4% men), of whom 105 also had diabetes. With the use of machine learning, the patients were assessed and categorized as having one of the three main types of cataracts: cortical (CC), nuclear (NS), and posterior subcapsular (PSC). A Random Forest Classification algorithm was employed to predict the incidence of different associations of cataracts (1, 2, or 3 types). Results: Cataracts have been encountered more frequently and at a younger age in patients with diabetes. CC was significantly more frequent among patients with diabetes (p < 0.0001), while the NS and PSC were only marginally, without statistical significance. Machine learning could also contribute to an early diagnosis of cataracts, with the presence of diabetes, duration of diabetes, or diabetic polyneuropathy (PND) having the highest importance for a successful classification. Conclusions: These findings suggest that diabetes may impact the type of cataract that develops, with CC being notably more prevalent in diabetic patients. This has important implications for screening and management strategies for cataract formation in diabetic populations.

PurposeTo determine the relationships between lens thickness (LT), lens density and anterior segment parameters in patients with mild to moderate cataracts.SettingOftalmosalud Instituto de Ojos, Lima, Perú.DesignProspective, single-centre, cross-sectional study.Methods169 eyes with age-related mild to moderate cataracts had lens density assessed using the Lens Opacification Classification System III, the built-in Pentacam HR Nucleus Staging software and ImageJ software. LT and axial length (AL) were measured with the IOLMaster 700, and angle parameters were measured using anterior segment optical coherence tomography. Pearson correlation coefficients and Kruskal-Wallis tests were used for statistical analyses.ResultsNuclear colour score was the only clinical parameter with a weak significant correlation with LT (r=0.24, p=0.003) after accounting for age, AL, gender and anterior chamber depth (ACD). The maximum value of average lens density and the mean nuclear density were significantly correlated with LT (r=0.24, p=0.003 and −0.17, p=0.03, respectively) after controlling for the same factors. Central LT greater than 4.48 mm was present in 54.5% of the eyes with a nuclear opalescence grade 1.ConclusionsLT is independent of lens density in mild to moderate cataracts after accounting for age, AL, ACD and gender contrary to previous studies.

Purpose. The previous lab and clinical studies of the correlation between the ultraviolet B and age-related cataract (ARC) did not reach in the universal agreement, especially in different morphological types of ARC. It is important to systemically summarize those previous data of epidemiological studies, which might penetrate the relevance between three morphological types of ARC, cortical, nuclear, and posterior capsular (PSC), with sunlight exposure. Methods. PubMed, Web of Science, CNKI, Embase, and Cochrane were searched online. Data were extracted and recalculated, and quality check was performed by hand. Review Manager was used to perform the fixed effects meta-analysis on ARC and its morphological types. The highest exposed dose group was defined as the exposed group, and the lowest dose group as the control group as possible. Results. Finally, the number of analyzed studies was 31: 20 for ARC and twelve, eleven, and nine for the morphological types cortical, nuclear, and PSC, respectively. The pooled OR for ARC was 1.15 (range 1.00~43.78, 95% CI 1.09 to 1.21). The cortical cataract revealed a slightly higher risk, and pooled OR was 1.03 (range 0.67~2.91, 95% CI 1.02 to 1.03). But the pooled OR for nuclear and PSC were 1.00 (range 0.50~5.35, 95% CI 1.00 to 1.00) and 0.99 (range 0.57~1.87, 95% CI 0.95 to 1.01), respectively. Conclusions. The systemic analysis of epidemiological articles reported till now reveals a significantly increased risk of ARC for those exposed with more sunlight, especially the morphological type of cortical cataract.

Background To assess the socio-demographic and health-related risk factors associated with cataract subtypes in Korea. Methods A total of 11,591 participants (aged ≥40 years) were selected from the Korean National Health and Nutrition Examination Survey between 2008 and 2010. The Korean Ophthalmologic Society conducted detailed ophthalmologic examinations on these participants based on the Lens Opacity Classification System III. Risk factors for developing any type of cataract, and its subtypes (nuclear, cortical, posterior subcapsular and mixed), were identified from univariate and multivariate logistic regression analysis. Results The prevalence of cataracts was 40.1% (95% CI, 37.8 − 42.3%) in participants over 40 years old. Older age, lower monthly household income, lower education, hypercholesterolemia, hypertension, and diabetes mellitus (DM) were independent risk factors for development of any cataract. Older age, lower monthly household income, lower education, hypercholesterolemia, and DM were independent risk factors for development of pure cortical cataracts. Older age, lower education, metabolic syndrome, and DM were independent risk factors for development of pure nuclear cataracts. Older age and DM were independent risk factors for development of pure posterior subcapsular cataracts. Older age, lower monthly household income, lower education, and DM were independent risk factors for development of mixed cataracts. Conclusion Although socioeconomic disparities are related to cataract development, this study identified several “modifiable” risk factors that may help to lower the incidence of cataracts and associated vision loss. Improved control of blood pressure, blood, glucose, and cholesterol may help to reduce the incidence of cataracts in the general Korean population.

Purpose To evaluate objectively intraocular scattering in eyes with nuclear, cortical and posterior subcapsular cataracts by means of an objective scatter index (OSI) obtained from double-pass images. To compare the results with those obtained using clinical conventional procedures. Methods In this prospective, observational, cross-sectional, non-consecutive case series study, 188 eyes with cataracts of 136 patients were analysed (123 eyes had nuclear, 41 eyes had cortical and 24 eyes had posterior subcapsular cataracts). The control group consisted of 117 eyes of 68 healthy patients. Patient examination included subjective refraction, best spectacle-corrected visual acuity (BSCVA), cataract grade using the lens opacities classification system III (LOCS III) and OSI. Results We found a decrease in the BSCVA and an increase in the OSI with increasing cataract grade. Statistically significant differences were observed when the OSI of eyes without cataracts and those with different LOCS III were compared. The comparison between the OSI and LOCS III reported good percentages of agreement regarding the number of eyes classified in equivalent levels: 72.4% (nuclear cataracts), 86.6% (cortical cataracts) and 84.3% (posterior subcapsular cataracts). A non-linear regression model was applied between OSI and BSCVA, which resulted in the following multiple correlation coefficients: r=0.878 (nuclear), r=0.843 (cortical) and r=0.844 (posterior subcapsular). Conclusions The results of the study showed that OSI is a useful parameter for evaluating large amounts of intraocular scattering that can be used, in combination with other conventional procedures, as a valuable tool in clinical practice to grade cataracts objectively.