Fundus Tessellation: Prevalence and Associated Factors

A preliminary study of artificial intelligence to recognize tessellated fundus in visual function screening of 7-14 year old students

BACKGROUND

To evaluate the accuracy of artificial intelligence (AI)-based technology in recognizing tessellated fundus in students aged 7-14 years.

METHODS

A retrospective study was conducted to collect consecutive fundus photographs for visual function screening of students aged 7-14 years old in Haikou City from June 2018 to May 2019, and 1907 cases were included in the study. Among them, 949 cases were male and 958cases were female. The results were manually analyzed by two attending ophthalmologists to ensure the accuracy of the results. In case of discrepancies between the results analyzed by the two methods, the manual results were used as the standard. To assess the sensitivity and specificity of AI in recognizing tessellated fundus, a Kappa consistency test was performed comparing the results of manual recognition with those of AI recognition.

RESULTS

Among 1907 cases, 1782 cases, or 93.4%, were completely consistent with the recognition results of manual and AI; 125 cases, or 6.6%, were analyzed with differences. The diagnostic rates of manual and AI for tessellated fundus were 26.1% and 26.4%, respectively. The sensitivity, specificity and area of the ROC curve (AUC) of AI for recognizing tessellated fundus in students aged 7-14 years were 88.0%, 95.4% and 0.917, respectively. The results of test showed that that the manual and AI identification results were highly consistent (κ = 0.831, P = 0.000).

CONCLUSION

AI analysis has high specificity and sensitivity for tessellated fundus identification in students aged 7-14 years, and it is feasible to apply artificial intelligence to visual function screening in students aged 7-14 years.

Age prediction using fundus parameters of normal eyes from the Kumejima population study

PURPOSE

Artificial intelligence can predict the age of an individual using color fundus photographs (CFPs). This study aimed to investigate the accuracy of age prediction in the Kumejima study using fundus parameters and to clarify age-related changes in the fundus.

METHODS

We used nonmydriatic CFPs obtained from the Kumejima population study, including 1,646 right eyes of healthy participants with reliable fundus parameter measurements. The tessellation fundus index was calculated as R/(R + G + B) using the mean value of the red-green-blue intensity in eight locations around the optic disc and foveal region. The optic disc ovality ratio, papillomacular angle, and retinal vessel angle were quantified as previously described. Least absolute shrinkage and selection operator regression with leave-one-out cross-validation was used to predict age. The relationship between the actual and predicted ages was investigated using Pearson's correlation coefficient.

RESULTS

The mean age of included participants (834 males and 812 females) was 53.4 ± 10.1 years. The mean predicted age based on fundus parameters was 53.4 ± 8.9 years, with a mean absolute error of 3.64 years, and the correlation coefficient between actual and predicted age was 0.88 (p < 0.001). Older patients had greater red and green intensities and weaker blue intensities in the peripapillary area (p < 0.001).

CONCLUSIONS

Age could be predicted using the CFP parameters, and there were notable age-related changes in the peripapillary color intensity. The age-related changes in the fundus may aid the understanding of the mechanism of fundus diseases such as age-related macular degeneration.

Relationship between fundus sex index obtained using color fundus parameters and body height or axial length in the Kumejima population

PURPOSE

To investigate the relationship between the fundus sex index obtained from fundus photographs and body height or axial length in the Kumejima population.

STUDY DESIGN

Prospective cross-sectional observational population study.

METHODS

Using color fundus photographs obtained from the Kumejima population, 1,653 healthy right eyes with reliable fundus parameter measurements were included in this study. The tessellation fundus index was calculated as R/(R + G + B) using the mean value of the red-green-blue intensity in the eight locations around the optic disc and foveal region. The optic disc ovality ratio, papillomacular angle, and retinal vessel angle were quantified as previously described. The masculine or feminine fundus was quantified using machine learning (L2 regularized binominal logistic regression and leave one out cross validation), with the range of 0-1 as the predictive value, and defined as the fundus sex index. The relationship between the fundus sex index and body height or axial length was investigated using Spearman's correlation.

RESULTS

The mean age of the 838 men and 815 women included in this study was 52.8 and 54.0 years, respectively. The correlation coefficient between fundus sex index and body height was - 0.40 (p < 0.001) in all, 0.01 (p = 0.89) in men, and - 0.04 (p = 0.30) in women, and that between fundus sex index and axial length was - 0.23 (p < 0.001) in all, - 0.12 (p < 0.001) in men, and - 0.13 (p < 0.001) in women.

CONCLUSION

This study shows that a larger number of masculine fundi tend to have longer axial lengths in each sex group. However, sex index was not significantly related with body height either in men or in women.

Choroidal vascularity features of fundus tessellation in adults with high myopia

BACKGROUND

To investigate alterations in choroidal vascularity index among highly myopic adults with fundus tessellation, utilizing optical coherence tomography.

METHODS

Total of 143 highly myopic adults (234 eyes) with fundus tessellation were collected in this cross-sectional study, which was stratified into different lesion groups based on the novel tessellated fundus classification. Subfoveal choroidal thickness (SFCT), choroidal luminal area (LA), stromal area (SA), total choroidal area (TCA), and choroidal vascularity index (CVI) were analyzed utilizing optical coherence tomography (OCT) with enhanced depth imaging (EDI) mode, enabling precise quantification of these parameters.

RESULTS

Comparison analysis demonstrated notable distinctions in spherical equivalent (SE), axial length (AL), and SFCT across the four tessellation grades (p < 0.001). Analysis of the choroidal vascularity parameters, including LA, TCA, and CVI, demonstrated notable disparities across the four groups (p < 0.001), while no significant variations were observed in SA when comparing Grade 1 versus Grade 2, as well as Grade 2 versus Grade 3 (p > 0.05). Logistic regression analyses illustrated that the higher grade of tessellated exhibited a positive association with AL (OR = 1.701, p = 0.027), while negatively associated with SFCT (OR = 0.416, p = 0.007), LA (OR = 0.438, p = 0.010) and CVI (OR = 0.529, p = 0.004). Multiple regression analyses demonstrated a significant negative association between CVI and both SE and AL after adjusting for age, while positively associated with SFCT (p < 0.05).

CONCLUSION

Subtle choroidal vascularity changes may have a meaningful contribution to the development and progression of fundus tessellation. CVI and LA dramatically decreased during the early stages of tessellation development and maintained a relatively stable status when in the severe tessellated grades.

Tessellated fundus occurs earlier than myopia in children aged 3-6 years

BACKGROUND/OBJECTIVES

Tessellated fundus can exist in normal healthy eyes. This study aims to evaluate the occurrence and influencing factors of tessellated fundus in preschool children aged 3-6 years.

SUBJECTS/METHODS

This kindergarten-based cross-sectional study included 1716 children with an age range of 3-6 years. All participants underwent a comprehensive eye examination and a questionnaire. According to the number of quadrants occupied by tessellated fundus around the optic disc in fundus photographs, it was divided into four grades.

RESULTS

600 (35.0%) children had peripapillary tessellation. According to the spherical equivalent (SE), the subjects were divided into three groups: Hyperopia group (SE > + 0.75D, n = 1194)；Pre-myopia group (-0.50D < SE ≤ + 0.75D, n = 455); Myopia group (SE ≤ -0.50D, n = 67). The proportion of peripapillary tessellated fundus was 33.0%, 38.0%, 50.7% respectively. According to the regression analysis, in the non-myopia group (Pre-myopia group and Hyperopia group), the occurrence of peripapillary tessellated fundus was associated with longer axial length (OR, 1.566; 95% CI: 1.229-1.996, p < 0.001) and larger corneal radius of curvature (OR, 1.837; 95% CI: 1.006-3.354, p = 0.048). However, in Pre-myopia group, the corneal radius of curvature was not associated with the occurrence of peripapillary tessellated fundus (p = 0.830). In Hyperopia group, the corneal radius of curvature was associated with the occurrence of peripapillary tessellated fundus (OR, 2.438; 95% CI: 1.160-5.122, p = 0.019).

CONCLUSIONS

The occurrence of peripapillary tessellated fundus is more than 30% in 3-6 year old preschool children. Tessellated fundus can also occur in non-myopic children, and is related to the length of axial length and large radius of corneal curvature.

Quantification of Fundus Tessellation Reflects Early Myopic Maculopathy in a Large-Scale Population of Children and Adolescents

Purpose

This study investigated the distribution of fundus tessellation density (FTD) in a Chinese pediatric population and its potential in reflecting early myopic maculopathy (tessellated fundus).

Methods

Participants were enrolled from kindergartens, primary schools, and middle schools, with cluster sampling in Shanghai, China. A series of ophthalmic examinations was conducted. Based on fundus photograph, FTD was quantitatively assessed using an artificial intelligence algorithm, and tessellated fundus was diagnosed by well-trained ophthalmologists.

Results

A total of 14,234 participants aged four to 18 years were included, with 7421 boys (52.1%). Tessellated fundus was observed in 2200 (15.5%) participants. The median of FTD was 0.86% (range 0.0-42.1%). FTD increased with age and axial length. In the logistics regression, larger FTD was independently associated with tessellated fundus (P < 0.001). The area under curves of receiver operating characteristic curve for categorizing tessellated fundus using FTD was 0.774, and the cutoff point of FTD was 2.22%.

Conclusions

The density of fundus tessellation was consistent with the severity of myopia. FTD could help diagnose the early stage of myopic maculopathy, tessellated fundus, providing a new pattern for myopia screening and detection of early myopic fundus changes.

Translational Relevance

Quantification of fundus tessellation with artificial intelligence could help detect early myopic maculopathy.

Fundus Tessellation and Parapapillary Atrophy, as Ocular Characteristics of Spontaneously High Myopia in Macaques: The Non-Human Primates Eye Study

Purpose

This study aimed to evaluate the ocular characteristics associated with spontaneously high myopia in adult nonhuman primates (NHPs).

Methods

A total of 537 eyes of 277 macaques with an average age of 18.53 ± 3.01 years (range = 5-26 years), raised in a controlled environment, were included. We measured ocular parameters, including spherical equivalent (SE), axial length (AXL), and intraocular pressure. The 45-degree fundus images centered on the macula and the disc assessed the fundus tessellation and parapapillary atrophy (PPA). Additionally, optical coherence tomography (OCT) was used to measure the thickness of the retinal nerve fiber layer (RNFL).

Results

The mean SE was -1.58 ± 3.71 diopters (D). The mean AXL was 18.76 ± 0.86 mm. The prevalence rate of high myopia was 17.7%. As myopia aggravated, the AXL increased (r = -0.498, P < 0.001). Compared with non-high myopia, highly myopic eyes had a greater AXL (P < 0.001), less RNFL thickness (P = 0.004), a higher incidence of PPA (P < 0.001), and elevated grades of fundus tessellation (P < 0.001). The binary logistic regression was performed, which showed PPA (odds ratio [OR] = 4.924, 95% confidence interval [CI] = 2.375-10.207, P < 0.001) and higher grades of fundus tessellation (OR = 1.865, 95% CI = 1.474-2.361, P < 0.001) were independent risk characteristics for high myopia.

Conclusions

In NHPs, a higher grade of fundus tessellation and PPA were significant biomarkers of high myopia.

Translational Relevance

The study demonstrates adult NHPs raised in conditioned rooms have a similar prevalence and highly consistent fundus changes with human beings, which strengthens the foundation for utilizing macaques as an animal model in high myopic studies.

Quantitative Assessment of Fundus Tessellated Density in Highly Myopic Glaucoma Using Deep Learning

Purpose

To characterize the fundus tessellated density (FTD) in highly myopic glaucoma (HMG) and high myopia (HM) for discovering early signs and diagnostic markers.

Methods

This retrospective cross-sectional study included hospital in-patients with HM (133 eyes) and HMG (73 eyes) with an axial length ≥26 mm at Zhongshan Ophthalmic Center. Using deep learning, FTD was quantified as the average exposed choroid area per unit area on fundus photographs in the global, macular, and disc regions. FTD-associated factors were assessed using partial correlation. Diagnostic efficacy was analyzed using the area under the curve (AUC).

Results

HMG patients had lower global (0.20 ± 0.12 versus 0.36 ± 0.09) and macular FTD (0.25 ± 0.14 vs. 0.40 ± 0.09) but larger disc FTD (0.24 ± 0.11 vs. 0.19 ± 0.07) than HM patients in the tessellated fundus (all P < 0.001). In the macular region, nasal FTD was lowest in the HM (0.26 ± 0.13) but highest in the HMG (0.32 ± 0.13) compared with the superior, inferior, and temporal subregions (all P < 0.05). A fundus with a macular region nasal/temporal (NT) FTD ratio > 0.96 (AUC = 0.909) was 15.7 times more indicative of HMG than HM. A higher macular region NT ratio with a lower horizontal parapapillary atrophy/disc ratio indicated a higher possibility of HMG than HM (AUC = 0.932).

Conclusions

FTD differs in degree and distribution between HMG and HM. A higher macular NT alone or with a lower horizontal parapapillary atrophy/disc ratio may help differentiate HMG.

Translational Relevance

Deep learning-based FTD measurement could potentially assist glaucoma diagnosis in HM.

Regional choroidal thickness estimation from color fundus images based on convolutional neural networks

Purpose

This study aims to estimate the regional choroidal thickness from color fundus images from convolutional neural networks in different network structures and task learning models.

Method

1276 color fundus photos and their corresponding choroidal thickness values from healthy subjects were obtained from the Topcon DRI Triton optical coherence tomography machine. Initially, ten commonly used convolutional neural networks were deployed to identify the most accurate model, which was subsequently selected for further training. This selected model was then employed in combination with single-, multiple-, and auxiliary-task training models to predict the average and sub-region choroidal thickness in both ETDRS (Early Treatment Diabetic Retinopathy Study) grids and 100-grid subregions. The values of mean absolute error and coefficient of determination (R2) were involved to evaluate the models' performance.

Results

Efficientnet-b0 network outperformed other networks with the lowest mean absolute error value (25.61 μm) and highest R2 (0.7817) in average choroidal thickness. Incorporating diopter spherical, anterior chamber depth, and lens thickness as auxiliary tasks improved predicted accuracy (p-value = 6.39 × 10 - 44 , 2.72 × 10 - 38 , 1.15 × 10 - 36 respectively). For ETDRS regional choroidal thickness estimation, multi-task model achieved better results than single task model (lowest mean absolute error = 31.10 μm vs. 33.20 μm). The multi-task training also can simultaneously predict the choroidal thickness of 100 grids with a minimum mean absolute error of 33.86 μm.

Conclusions

Efficientnet-b0, in combination with multi-task and auxiliary task models, achieve high accuracy in estimating average and regional macular choroidal thickness directly from color fundus photographs.

ASSOCIATION OF TESSELLATION DENSITY WITH PROGRESSION OF AXIAL LENGTH AND REFRACTION IN CHILDREN: An Artificial Intelligence-Assisted 4-Year Study

PURPOSE

To investigate fundus tessellation density (TD) and its association with axial length (AL) elongation and spherical equivalent (SE) progression in children.

METHODS

The school-based prospective cohort study enrolled 1,997 individuals aged 7 to 9 years in 11 elementary schools in Mojiang, China. Cycloplegic refraction and biometry were performed at baseline and 4-year visits. The baseline fundus photographs were taken, and TD, defined as the percentage of exposed choroidal vessel area in the photographs, was quantified using an artificial intelligence-assisted semiautomatic labeling approach. After the exclusion of 330 ineligible participants because of loss to follow-up or ineligible fundus photographs, logistic models were used to assess the association of TD with rapid AL elongation (>0.36 mm/year) and SE progression (>1.00 D/year).

RESULTS

The prevalence of tessellation was 477 of 1,667 (28.6%) and mean TD was 0.008 ± 0.019. The mean AL elongation and SE progression in 4 years were 0.90 ± 0.58 mm and -1.09 ± 1.25 D. Higher TD was associated with longer baseline AL (β, 0.030; 95% confidence interval: 0.015-0.046; P < 0.001) and more myopic baseline SE (β, -0.017; 95% confidence interval: -0.032 to -0.002; P = 0.029). Higher TD was associated with rapid AL elongation (odds ratio, 1.128; 95% confidence interval: 1.055-1.207; P < 0.001) and SE progression (odds ratio, 1.123; 95% confidence interval: 1.020-1.237; P = 0.018).

CONCLUSION

Tessellation density is a potential indicator of rapid AL elongation and refractive progression in children. TD measurement could be a routine to monitor AL elongation.

Association between Fundus Tessellation and Contrast Sensitivity in Myopic Eyes

PURPOSE

To assess the association of fundus tessellation with contrast sensitivity, Quality of Vision questionnaire, and other factors at five years postcorneal refractive surgery.

METHODS

This is a cross-sectional study. Both eyes of 98 subjects (196 eyes) who received femtosecond laser in situ keratomileusis (FS-LASIK) or small incision lenticular extraction (SMILE) five years prior were enrolled in this study. Fundus tessellation was imaged using wide-angle fundus photographs and graded into four categories with the assistance of the ETDRS grid. Photopic and mesopic contrast sensitivity were measured under the best correction. The Quality of Vision (QoV) questionnaire was used to assess visual symptoms.

RESULTS

Fundus tessellation was classified as follows: 19 eyes were grade 0 (9.7%), 28 eyes were grade 1 (14.3%), 59 eyes were grade 2 (30.1%), and 90 eyes were grade 3 (45.9%). Higher degrees of fundus tessellation were associated with lower photopic contrast sensitivity, a significant difference was observed at spatial frequencies of 6cpd (p = 0.030, grade 1 >grade 3 p = 0.011). Higher degrees of fundus tessellation were also associated with lower mesopic contrast sensitivity, a significant difference was observed at spatial frequencies of 18cpd (p = 0.011, grade 0 >grade 3 p = 0.012). The preoperative degree of myopia was positively associated with fundus tessellation grade (p < 0.001). However, in linear mixed-effect model analysis, no significant influence of parameters (contrast sensitivity, preoperative myopia, and QoV scores) upon different tessellation grades was found (p > 0.05).

CONCLUSIONS

Patients with moderate and high myopia were more likely to have higher grades of fundus tessellation. Higher degree of fundus tessellation associates with lower contrast sensitivity. Patients with moderate and high myopia should be concerned with retinal-choroidal changes. Contrast sensitivity could be a clinical sign for progression of tessellation and used to screen for early retinal-choroidal changes to prevent pathologic myopia.

DeepGraFT: A novel semantic segmentation auxiliary ROI-based deep learning framework for effective fundus tessellation classification

Fundus tessellation (FT) is a prevalent clinical feature associated with myopia and has implications in the development of myopic maculopathy, which causes irreversible visual impairment. Accurate classification of FT in color fundus photo can help predict the disease progression and prognosis. However, the lack of precise detection and classification tools has created an unmet medical need, underscoring the importance of exploring the clinical utility of FT. Thus, to address this gap, we introduce an automatic FT grading system (called DeepGraFT) using classification-and-segmentation co-decision models by deep learning. ConvNeXt, utilizing transfer learning from pretrained ImageNet weights, was employed for the classification algorithm, aligning with a region of interest based on the ETDRS grading system to boost performance. A segmentation model was developed to detect FT exits, complementing the classification for improved grading accuracy. The training set of DeepGraFT was from our in-house cohort (MAGIC), and the validation sets consisted of the rest part of in-house cohort and an independent public cohort (UK Biobank). DeepGraFT demonstrated a high performance in the training stage and achieved an impressive accuracy in validation phase (in-house cohort: 86.85 %; public cohort: 81.50 %). Furthermore, our findings demonstrated that DeepGraFT surpasses machine learning-based classification models in FT classification, achieving a 5.57 % increase in accuracy. Ablation analysis revealed that the introduced modules significantly enhanced classification effectiveness and elevated accuracy from 79.85 % to 86.85 %. Further analysis using the results provided by DeepGraFT unveiled a significant negative association between FT and spherical equivalent (SE) in the UK Biobank cohort. In conclusion, DeepGraFT accentuates potential benefits of the deep learning model in automating the grading of FT and allows for potential utility as a clinical-decision support tool for predicting progression of pathological myopia.

Prevalence and Associations of Peripheral Arterial Disease in China: The Beijing Eye Study

PURPOSE

To explore the prevalence and associations of peripheral arterial disease (PAD) in China.

DESIGN

Population-based incidence estimate and cross-sectional study.

METHODS

The participants (n=3468) of the Beijing Eye Study underwent a detailed ophthalmologic and systemic examination including assessment of the ankle-brachial index (ABI). PAD was defined by an ABI of less than 0.9.

RESULTS

Blood pressure measurements of both arms and ankles were available for 1078 (31.1%) individuals. An ABI (mean: 1.09±0.11; median: 1.10; range: 0.25, 1.36) of <0.9 and <0.95 was found in 32 of 1078 participants (3.0%, 95% CI 2.0, 4.0) and 70 of 1078 individuals (6.5%, 95% CI 5.0, 8.0), respectively. Higher PAD prevalence (multivariable analysis) was associated with older age (odds ratio [OR] 1.08, 95% CI 1.02, 1.15; P = .009), lower level of education (OR 0.62, 95% CI 0.43, 0.90; P = .01), lower quality of life (OR 0.67, 95% CI 1.11, 2.32), higher glucose serum concentration (OR 1.36, 95% CI 1.09, 1.58; P = .006), lower estimated glomerular filtration rate (OR 0.98, 95% CI 0.96, 0.99; P = .04), and higher prevalence of retinal vein occlusions (OR 7.30, 95% CI 1.63, 32.6; P = .009). PAD prevalence was not associated with the prevalence of glaucoma (P = .53) (open-angle glaucoma: P = .42; angle-closure glaucoma: P = .57) and age-related macular degeneration (any AMD: P = .39; early AMD: P = .31; intermediate AMD: P = .92; late AMD: P = .99), prevalence (P = .26) and stage (P = .07) of diabetic retinopathy, prevalence (P = .38) and degree (P = .68) of nuclear cataract, prevalence (P = .39) and degree (P = .72) of cortical cataract, prevalence of subcapsular cataract (P = .86), prevalence of pseudoexfoliation (P = .65), intraocular pressure (P = .50), axial length (P = .56), and peripapillary retinal nerve fiber layer thickness (P = .68).

CONCLUSIONS

The PAD prevalence (3.0%, 95% CI 2.0%, 4.0%) was relatively low in this cohort from rural and urban Beijing, with older age, lower educational level, lower quality of life, higher glucose serum concentration, lower estimated glomerular filtration rate, and higher prevalence of retinal vein occlusions as main associated factors.

Arterial hypertension and retinal layer thickness: the Beijing Eye Study

PURPOSE

To investigate relationships between blood pressure and the thickness of single retinal layers in the macula.

METHODS

Participants of the population-based Beijing Eye Study, free of retinal or optic nerve disease, underwent medical and ophthalmological examinations including optical coherence tomographic examination of the macula. Applying a multiple-surface segmentation solution, we automatically segmented the retina into its various layers.

RESULTS

The study included 2237 participants (mean age 61.8±8.4 years, range 50-93 years). Mean thicknesses of the retinal nerve fibre layer (RNFL), ganglion cell layer (GCL), inner plexiform layer, inner nuclear layer (INL), outer plexiform layer, outer nuclear layer/external limiting membrane, ellipsoid zone, photoreceptor outer segments (POS) and retinal pigment epithelium-Bruch membrane were 31.1±2.3 µm, 39.7±3.5 µm, 38.4±3.3 µm, 34.8±2.0 µm, 28.1±3.0 µm, 79.2±7.3 µm, 22.9±0.6 µm, 19.2±3.3 µm and 20.7±1.4 µm, respectively. In multivariable analysis, higher systolic blood pressure (SBP) and diastolic blood pressure (DBP) were associated with thinner GCL and thicker INL, after adjusting for age, sex and axial length (all p<0.0056). Higher SBP was additionally associated with thinner POS and higher DBP with thinner RNFL. For an elevation of SBP/DBP by 10 mm Hg, the RNFL, GCL, INL and POS changed by 2.0, 3.0, 1.5 and 2.0 µm, respectively.

CONCLUSIONS

Thickness of RNFL, GCL and POS was inversely and INL thickness was positively associated with higher blood pressure, while the thickness of the other retinal layers was not significantly correlated with blood pressure. The findings may be helpful for refinement of the morphometric detection of retinal diseases.

Clinical Features of Fundus Tessellation and Its Relationship with Myopia: A Systematic Review and Meta-analysis

INTRODUCTION

This study aims to assess the existing literature on fundus tessellation (FT), focusing on its prevalence, associated factors, distribution, and progression.

METHODS

Systemic methods were employed to search and gather published literature on FT from databases such as the National Library of Medicine (PubMed), Web of Science (WOS), and Elsevier on July 1, 2023. The quality of the studies was evaluated using the Newcastle-Ottawa Scale (NOS) and the Healthcare Research and Quality (AHRQ) criteria. A meta-analysis was conducted to compare tessellated and normal fundus with respect to age, gender, axial length, and spherical equivalent.

RESULTS

The systematic review included 23 articles, encompassing a total of 3053 eyes in the meta-analysis. The prevalence of FT varied from 43.00 to 94.35%. The severity of FT was significantly associated with older age, male sex, lower body weight index, longer axial length, larger peripapillary atrophy, thinner choroid, thinner sclera, and larger corneal radius of curvature, suggesting a potential progression pattern. Notably, FT was observed predominantly in the macular and peripapillary regions. The meta-analysis revealed that tessellated fundus tended to be associated with older age (mean difference [MD] 4.76, 95% confidence interval [CI] 1.71-7.80, P < 0.01), longer axial length (MD 0.86, 95% CI 0.70-1.02, P < 0.01), and a lower spherical equivalent (MD - 1.16, 95% CI - 1.68 to 0.65, P < 0.01) compared to normal fundus. However, there was no significant difference in the proportion of males between individuals with tessellated and normal fundus (odds ratio [OR] 1.12, 95% CI 0.89-1.42, P = 0.32).

CONCLUSIONS

Overall, this systematic review and meta-analysis shed light on the prevalence, characteristics, and factors associated with FT, offering valuable insights for clinicians and researchers in the field of ophthalmology.

STUDY REGISTRATION

The study protocol was registered on the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42023442486).

Three-year changes in sex judgment using color fundus parameters in elementary school students

PURPOSE

In a previous cross-sectional study, we reported that the sexes can be distinguished using known factors obtained from color fundus photography (CFP). However, it is not clear how sex differences in fundus parameters appear across the human lifespan. Therefore, we conducted a cohort study to investigate sex determination based on fundus parameters in elementary school students.

METHODS

This prospective observational longitudinal study investigated 109 right eyes of elementary school students over 4 years (age, 8.5 to 11.5 years). From each CFP, the tessellation fundus index was calculated as red/red + green + blue (R/[R+G+B]) using the mean value of red-green-blue intensity in eight locations around the optic disc and macular region. Optic disc area, ovality ratio, papillomacular angle, and retinal vessel angles and distances were quantified according to the data in our previous report. Using 54 fundus parameters, sex was predicted by L2 regularized binomial logistic regression for each grade.

RESULTS

The right eyes of 53 boys and 56 girls were analyzed. The discrimination accuracy rate significantly increased with age: 56.3% at 8.5 years, 46.1% at 9.5 years, 65.5% at 10.5 years and 73.1% at 11.5 years.

CONCLUSIONS

The accuracy of sex discrimination by fundus photography improved during a 3-year cohort study of elementary school students.

Fundus Tessellated Density of Pathologic Myopia

PURPOSE

The study aimed to quantitatively evaluate the fundus tessellated density (FTD) in different categories of pathologic myopia (PM) using fundus photographs with the application of artificial intelligence.

METHODS

A retrospective review of 407 PM (META-PM, Category 2-Category 4) eyes was conducted, employing a biomimetic mechanism of human vision and integrated image processing technologies for FTD extraction and calculation. Different regions of interest were analyzed, including circle O4.5 (optic disc centered, diameter of 4.5 mm) and circle M1.0, M3.0, M6.0 (macular centered, diameter of 1.0, 3.0, and 6.0 mm), using 2 partitioning methods ("X" and "+"). The density of patchy (Category 3) or macular atrophy (Category 4) areas was quantified. Univariate and multivariate linear regression analyses were performed to assess the association with FTD.

RESULTS

The mean FTD of total PM eyes was 0.283, ranging from 0.002 to 0.500, and demonstrating a negative correlation with the PM category. In multivariate analysis, age was found to be significantly associated with FTD ( P <0.05), while axial length did not show a significant association. Fundus tessellation of circle O4.5 and circle M6.0 displayed associations with the FTD across different PM categories. The "X" partitioning method better fit the circle M6.0 region, while both methods were suitable for the circle O4.5 region. After excluding the patchy and macular atrophic areas, the mean FTD values were 0.346 in Category 2, 0.261 in Category 3, and 0.186 in Category 4.

CONCLUSIONS

The study revealed a decreasing trend in FTD values across different categories of PM, regardless of the presence or absence of patchy or macular atrophic areas. Quantifying FTD in PM could be a valuable tool for improving the existing PM classification system and gaining insights into the origin of posterior staphyloma and visual field defects in high myopia.

Spontaneously Myopic Guinea Pig: Model of Early Pathologic Myopia

Purpose

To evaluate whether pigmented guinea pigs with spontaneous myopia present characteristic changes of pathologic myopia.

Methods

The fundus images of guinea pigs (3 weeks old) were graded according to fundus tessellation (FT) degree. Biometric parameters, including refraction, vitreous chamber depth (VCD), and axial length (AL), were measured at ages 21 and 43 days. Some of these animals were divided into three groups: hyperopic without FT (H w/o FT), myopic without FT (M w/o FT), and myopic with FT (M w/ FT). The horizontal and vertical radii of curvature of posterior sclera (RP-H and RP-V, respectively) and the radii of curvature and arc lengths of superior sclera (RS and LS, respectively), inferior sclera (RI and LI, respectively), nasal sclera (RN and LN, respectively), and temporal sclera (RT and LT) were evaluated by Fuji.

Results

The fundi were graded as type A or type B (both without FT), type C (mild FT), or type D (severe FT). The prevalence of FT was correlated with myopic refraction, longer VCD, and longer AL. Eyes of M w/FT animals had shorter RP-H and RP-V, longer RS and RT, and longer LS and LT than eyes of H w/o FT or M w/o FT animals. Refractions shifted toward hyperopia in eyes lacking FT, but not in eyes having FT. The changes in VCD were consistent with the changes in refraction. This relatively myopic shift in refraction and shortening of VCD were found only in myopic eyes with FT, but not in myopic eyes without FT.

Conclusions

Spontaneously myopic guinea pig eyes have a high prevalence of FT. Myopic eyes with FT presented characteristic signs of pathologic myopia.

Prevalence of Fundus Tessellation and Its Screening Based on Artificial Intelligence in Chinese Children: the Nanjing Eye Study

INTRODUCTION

To investigate the prevalence of fundus tessellation (FT), and the threshold for screening FT using an artificial intelligence (AI) technology in Chinese children.

METHODS

The Nanjing Eye Study was a population-based cohort study conducted in children born between September 2011 and August 2012 in Yuhuatai District of Nanjing. The data presented in this paper were obtained in 2019, when these children were 7 years old and underwent 45° non-mydriatic fundus photography. FT in whole fundus, macular area, and peripapillary area was manually recognized from fundus photographs and classified into three grades. Fundus tessellation density (FTD) in these areas was obtained by calculating the average exposed choroid area per unit area using artificial intelligence (AI) technology based on fundus photographs. The threshold for screening FT using FTD was determined using receiver operating characteristic (ROC) curve analysis.

RESULTS

Among 1062 enrolled children (mean [± standard deviation] spherical equivalent: - 0.28 ± 0.70 D), the prevalence of FT was 42.18% in the whole fundus (grade 1: 36.53%; grade 2: 5.08%; grade 3: 0.56%), 45.57% in macular area (grade 1: 43.5%; grade 2: 1.60%; grade 3: 0.50%), and 49.72% in peripapillary area (grade 1: 44.44%; grade 2: 4.43%; grade 3: 0.85%), respectively. The threshold value of FTD for screening severe FT (grade ≥ 2) was 0.049 (area under curve [AUC] 0.985; sensitivity 98.3%; specificity 92.3%) in the whole fundus, 0.069 (AUC 0.987; sensitivity 95.5%; specificity 96.2%) in the macular area, and 0.094 (AUC 0.980; sensitivity 94.6%; specificity 94.2%) in the peripapillary area, respectively.

CONCLUSION

Fundus tessellation affected approximately 40 in 100 children aged 7 years in China, indicating the importance and necessity of early FT screening. The threshold values of FTD provided by this study had high accuracy for detecting severe FT and might be applied for rapid screening.

Role of corneal radius of curvature in early identification of fundus tessellation in children with low myopia

AIM

To assess the role of the corneal radius of curvature (CR) in the identification of fundus tessellation in children with low myopia.

METHODS

In the cross-sectional study, students aged 9-12 years from 24 primary schools in Shanghai were enrolled by cluster sampling. Participants underwent measurements including cycloplegic refraction and axial length. Fundus images and choroidal thickness were obtained by swept-source optical coherence tomography. Fundus tessellation was classified into four grades according to fundus photographs.

RESULTS

A total of 1127 children with low myopia (spherical equivalence (SE) >-3.00 dioptre (D) but ≤-0.50 D) were included, with a mean age of 10.29±0.60 years and a mean SE of -1.44±0.69 D. Fundus tessellation was found in 591 (52.4%) cases (grade 1: 428, 38.0%; grade 2: 128, 11.4%; grade 3: 35, 3.1%). Choroidal thickness decreased as fundus tessellation grade increased (p trend <0.001). According to regression analysis, higher fundus tessellation grade was independently associated with larger CR (OR, 7.499; 95% CI 2.279 to 24.675, p=0.001). For those with CR >7.9 mm, along with CR, degree and proportion of fundus tessellation increased sharply.

CONCLUSION

Fundus tessellation existed in more than half of children with low myopia. Preliminary fundus photography conducted in children with low myopia with large CR would be necessary and beneficial to the early management of myopic fundus changes. Trial registration number NCT02980445.

Myopic Vascular Changes Revealed by Optical Tomography Angiography and Their Association with Myopic Fundus Manifestations

INTRODUCTION

We aimed to quantify and evaluate fundal vascular changes at different severities of myopia using optical tomography angiography (OCTA) and explore their association with fundus changes captured by ultra-widefield (UWF) fundus cameras.

METHODS

Seventy-four participants with myopia were enrolled in the study and underwent basic ophthalmic examination, OCTA, and UWF fundus photography. Multiple parameters were obtained using OCTA (flow area, structure thickness, and vessel density) and UWF fundus cameras (tessellation and parapapillary atrophy [PPA]).

RESULTS

The right eye of 30 participants with low and moderate myopia and 44 participants with high myopia (HM) were included. Patients with HM had a larger flow area of the outer retina (FA-OR) and a smaller thickness of choroid (TC). Axial length was significantly correlated with retinal and choroidal flow area and thickness in the different zones. The PPA area was positively correlated with FA-OR and negatively correlated with TC. Tessellation exhibited different levels of correlation with OCTA parameters regarding the flow area, thickness, and vessel density of the fundal layers, mainly in the inner retina.

CONCLUSION

FA-OR and TC exhibited sensitive changes in patients with HM and axial elongation; therefore, they could serve as predictive OCTA biomarkers. The PPA and tessellation were connected to the vascular and structural changes revealed by OCTA.

Sex determination using color fundus parameters in older adults of Kumejima population study

PURPOSE

Deep learning artificial intelligence can determine the sex using only fundus photographs. However, the factors used by deep learning to determine the sex are not visible. Therefore, the purpose of the study was to determine whether the sex of an older individual can be determined by regression analysis of their color fundus photographs (CFPs).

METHODS

Forty-two parameters were analyzed by regression analysis using 1653 CFPs of normal subjects in the Kumajima study. The parameters included the mean values of red, green, and blue intensities; the tessellation fundus index; the optic disc ovality ratio; the papillomacular angle; and the retinal vessel angles. Finally, the L2 regularized binomial logistic regression was used to predict the sex using all the parameters, and the diagnostic ability was assessed through the leave-one-cross-validation.

RESULTS

The mean age of the 838 men and 815 women were 52.8 and 54.0 years, respectively. The ovality ratio and retinal artery angles in women were significantly smaller than that in men. The green intensity at all locations for the women were significantly higher than that of men (P < 0.001). The discrimination accuracy rate assessed by the area-under-the-curve was 80.4%.

CONCLUSIONS

Our methods can determine the sex from the CFPs of the adult with an accuracy of 80.4%. The ovality ratio, retinal vessel angles, tessellation, and the green intensities of the fundus are important factors to identify the sex in individuals over 40 years old.

mTORC1 Signaling and Negative Lens-Induced Axial Elongation

Purpose

The mechanism underlying axial elongation during myopia progression remains unknown. Epidermal growth factor receptor (EGFR) signaling is associated with axial elongation. We explored whether mammalian target of rapamycin complex 1 (mTORC1) signaling acts as the downstream pathway of EGFR and participates in negative lens-induced axial elongation (NLIAE).

Methods

Three-week-old male pigmented guinea pigs underwent binocular NLIAE. (1) To investigate whether EGFR is the upstream regulator of mTORC1, an EGFR inhibitor (20 µg erlotinib) was intravitreally injected once a week for three weeks. (2) To assess the effect of mTORC1 inhibition on NLIAE, an mTORC1 inhibitor (2 µg, 10 µg, and 20 µg everolimus) was intravitreally injected once a week for three weeks. (3) To explore the long-term effect of mTORC1 overactivation on axial elongation, an mTORC1 agonist (4 µg MHY1485) was intravitreally injected once a week for three months. Biometric measurements included axial length and choroidal thickness were performed.

Results

Compared with the guinea pigs without NLIAE, NLIAE was associated with activation of mTORC1 signaling, which was suppressed by intravitreal erlotinib injection. Intravitreally injected everolimus suppressed NLIAE-induced axial elongation, mTORC1 activation, choroidal thinning, and hypoxia-inducible factor-1α expression in the sclera. Immunofluorescence revealed that the retinal pigment epithelium was the primary location of mTORC1 activation during NLIAE. Combining NLIAE and MHY1485 intravitreal injections significantly promoted axial elongation, choroidal thinning, and peripapillary choroidal atrophy.

Conclusions

The mTORC1 signaling is associated with increased axial elongation, as in NLIAE, raising the possibility of inhibiting mTORC1 as a novel treatment for slowing myopia progression.

Relationships between quantitative retinal microvascular characteristics and cognitive function based on automated artificial intelligence measurements

Introduction: The purpose of this study is to assess the relationship between retinal vascular characteristics and cognitive function using artificial intelligence techniques to obtain fully automated quantitative measurements of retinal vascular morphological parameters. Methods: A deep learning-based semantic segmentation network ResNet101-UNet was used to construct a vascular segmentation model for fully automated quantitative measurement of retinal vascular parameters on fundus photographs. Retinal photographs centered on the optic disc of 3107 participants (aged 50-93 years) from the Beijing Eye Study 2011, a population-based cross-sectional study, were analyzed. The main parameters included the retinal vascular branching angle, vascular fractal dimension, vascular diameter, vascular tortuosity, and vascular density. Cognitive function was assessed using the Mini-Mental State Examination (MMSE). Results: The results showed that the mean MMSE score was 26.34 ± 3.64 (median: 27; range: 2-30). Among the participants, 414 (13.3%) were classified as having cognitive impairment (MMSE score < 24), 296 (9.5%) were classified as mild cognitive impairment (MMSE: 19-23), 98 (3.2%) were classified as moderate cognitive impairment (MMSE: 10-18), and 20 (0.6%) were classified as severe cognitive impairment (MMSE < 10). Compared with the normal cognitive function group, the retinal venular average diameter was significantly larger (p = 0.013), and the retinal vascular fractal dimension and vascular density were significantly smaller (both p < 0.001) in the mild cognitive impairment group. The retinal arteriole-to-venular ratio (p = 0.003) and vascular fractal dimension (p = 0.033) were significantly decreased in the severe cognitive impairment group compared to the mild cognitive impairment group. In the multivariate analysis, better cognition (i.e., higher MMSE score) was significantly associated with higher retinal vascular fractal dimension (b = 0.134, p = 0.043) and higher retinal vascular density (b = 0.152, p = 0.023) after adjustment for age, best corrected visual acuity (BCVA) (logMAR) and education level. Discussion: In conclusion, our findings derived from an artificial intelligence-based fully automated retinal vascular parameter measurement method showed that several retinal vascular morphological parameters were correlated with cognitive impairment. The decrease in retinal vascular fractal dimension and decreased vascular density may serve as candidate biomarkers for early identification of cognitive impairment. The observed reduction in the retinal arteriole-to-venular ratio occurs in the late stages of cognitive impairment.

A deep network using coarse clinical prior for myopic maculopathy grading

Pathological Myopia (PM) is a globally prevalent eye disease which is one of the main causes of blindness. In the long-term clinical observation, myopic maculopathy is a main criterion to diagnose PM severity. The grading of myopic maculopathy can provide a severity and progression prediction of PM to perform treatment and prevent myopia blindness in time. In this paper, we propose a feature fusion framework to utilize tessellated fundus and the brightest region in fundus images as prior knowledge. The proposed framework consists of prior knowledge extraction module and feature fusion module. Prior knowledge extraction module uses traditional image processing methods to extract the prior knowledge to indicate coarse lesion positions in fundus images. Furthermore, the prior, tessellated fundus and the brightest region in fundus images, are integrated into deep learning network as global and local constrains respectively by feature fusion module. In addition, rank loss is designed to increase the continuity of classification score. We collect a private color fundus dataset from Beijing TongRen Hospital containing 714 clinical images. The dataset contains all 5 grades of myopic maculopathy which are labeled by experienced ophthalmologists. Our framework achieves 0.8921 five-grade accuracy on our private dataset. Pathological Myopia (PALM) dataset is used for comparison with other related algorithms. Our framework is trained with 400 images and achieves an AUC of 0.9981 for two-class grading. The results show that our framework can achieve a good performance for myopic maculopathy grading.

Prevalence and Characteristics of Myopia in Adult Rhesus Macaques in Southwest China

Purpose

To investigate the prevalence of myopia in a large cohort of adult rhesus macaques at Yunnan Province in southwest China and describe the characteristics of myopic rhesus macaque eyes.

Methods

A total of 219 rhesus macaques 14.07 ± 2.72 years old (range, 8-21) were randomly recruited for this study. We performed fundus photography and measurements of cycloplegic refractive error (RE) and axial length (AL) on macaques.

Results

A total of 429 eyes of 219 macaques were examined. The median RE was -1.25 diopters (D), and the median AL was 18.69 mm. The prevalence of myopia was 62.47%, and one-third of the myopic eyes were highly myopic. The presence of fundus tessellations was higher in myopic eyes than non-myopic eyes (42.54% vs. 6.21%). The cutoff value for the presence of tessellations was -3.52 D for RE and 19.38 mm for AL. In myopic eyes, there were significant differences between grade 1 and grade 3 fundus tessellations on RE (-5.57 ± 2.97 D vs. -8.13 ± 3.51 D) and AL (19.66 ± 0.55 mm vs. 20.60 ± 1.06 mm). Beta-peripapillary atrophy (β-PPA) was found in 48.10% of myopic eyes and 6.83% of non-myopic eyes. The presence of β-PPA is associated with the presence of fundus tessellations, AL, and RE. The presence of β-PPA was higher in grade 3 than grade 1 fundus tessellations (94.4% vs. 76%).

Conclusions

More than half of adult rhesus macaques in southwest China are myopic, and one-third of the myopic ones are highly myopic. Similar to humans, tessellated fundi and β-PPA are the characteristic signs of myopic rhesus macaques. Adult rhesus macaques are optimal animal models for research on the pathogenesis of myopia.

Translational Relevance

This study not only provides a reference for the refractive state and AL in myopic rhesus macaques but also indicates that adult rhesus macaques with spontaneous myopia are optimal animal models for research on the pathogenesis of myopia.

Machine Learning-Based Integration of Metabolomics Characterisation Predicts Progression of Myopic Retinopathy in Children and Adolescents

Myopic retinopathy is an important cause of irreversible vision loss and blindness. As metabolomics has recently been successfully applied in myopia research, this study sought to characterize the serum metabolic profile of myopic retinopathy in children and adolescents (4-18 years) and to develop a diagnostic model that combines clinical and metabolic features. We selected clinical and serum metabolic data from children and adolescents at different time points as the training set (n = 516) and the validation set (n = 60). All participants underwent an ophthalmologic examination. Untargeted metabolomics analysis of serum was performed. Three machine learning (ML) models were trained by combining metabolic features and conventional clinical factors that were screened for significance in discrimination. The better-performing model was validated in an independent point-in-time cohort and risk nomograms were developed. Retinopathy was present in 34.2% of participants (n = 185) in the training set, including 109 (28.61%) with mild to moderate myopia. A total of 27 metabolites showed significant variation between groups. After combining Lasso and random forest (RF), 12 modelled metabolites (mainly those involved in energy metabolism) were screened. Both the logistic regression and extreme Gradient Boosting (XGBoost) algorithms showed good discriminatory ability. In the time-validation cohort, logistic regression (AUC 0.842, 95% CI 0.724-0.96) and XGBoost (AUC 0.897, 95% CI 0.807-0.986) also showed good prediction accuracy and had well-fitted calibration curves. Three clinical characteristic coefficients remained significant in the multivariate joint model (p < 0.05), as did 8/12 metabolic characteristic coefficients. Myopic retinopathy may have abnormal energy metabolism. Machine learning models based on metabolic profiles and clinical data demonstrate good predictive performance and facilitate the development of individual interventions for myopia in children and adolescents.

Choroidal vascularity index in leptochoroid: A comparative analysis between reticular pseudodrusen and high myopia

PURPOSE

To investigate the choroidal vascularity index (CVI) in patients affected by leptochoroid.

METHODS

Three distinct age-matched cohorts were collected: patients with reticular pseudodrusen (RPD) secondary to age-related macular degeneration, patients with high-myopia, and healthy controls. CVI was calculated in the subfoveal 6000 μm diameter area.

RESULTS

54 eyes (54 patients) were included (18 eyes in each cohort). No statistical differences were disclosed in terms of age between controls, RPD patients (p = 0.062), and myopic patients (p = 0.070). Total choroidal area showed a different distribution among the 3 cohorts (p < 0.001), due to the reduction of luminal and stromal choroidal area in both RPD and myopic groups in comparison to controls (p < 0.001). Interestingly, CVI showed a different distribution between the 3 cohorts (p < 0.001). In detail, RPD group showed no changes in CVI in comparison to controls (p = 1.000), whereas the myopic group showed a higher CVI in comparison to both RPD group and controls (p < 0.001 in both analyses).

CONCLUSIONS

Different changes of the choroidal vascular and stromal components characterize the leptochoroid secondary to RPD eyes and high-myopic eyes. The relative greater impairment of the vascular area in RPD eyes in comparison to myopic eyes could be at the basis of the lower development of RPD in patients with high myopia.

Efficacy of a Deep Learning System for Screening Myopic Maculopathy Based on Color Fundus Photographs

INTRODUCTION

The maculopathy in highly myopic eyes is complex. Its clinical diagnosis is a huge workload and subjective. To simply and quickly classify pathologic myopia (PM), a deep learning algorithm was developed and assessed to screen myopic maculopathy lesions based on color fundus photographs.

METHODS

This study included 10,347 ocular fundus photographs from 7606 participants. Of these photographs, 8210 were used for training and validation, and 2137 for external testing. A deep learning algorithm was trained, validated, and externally tested to screen myopic maculopathy which was classified into four categories: normal or mild tessellated fundus, severe tessellated fundus, early-stage PM, and advanced-stage PM. The area under the precision-recall curve, the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, and Cohen's kappa were calculated and compared with those of retina specialists.

RESULTS

In the validation data set, the model detected normal or mild tessellated fundus, severe tessellated fundus, early-stage PM, and advanced-stage PM with AUCs of 0.98, 0.95, 0.99, and 1.00, respectively; while in the external-testing data set of 2137 photographs, the model had AUCs of 0.99, 0.96, 0.98, and 1.00, respectively.

CONCLUSIONS

We developed a deep learning model for detection and classification of myopic maculopathy based on fundus photographs. Our model achieved high sensitivities, specificities, and reliable Cohen's kappa, compared with those of attending ophthalmologists.

Three-Dimensional Multi-Task Deep Learning Model to Detect Glaucomatous Optic Neuropathy and Myopic Features From Optical Coherence Tomography Scans: A Retrospective Multi-Centre Study

Purpose

We aim to develop a multi-task three-dimensional (3D) deep learning (DL) model to detect glaucomatous optic neuropathy (GON) and myopic features (MF) simultaneously from spectral-domain optical coherence tomography (SDOCT) volumetric scans.

Methods

Each volumetric scan was labelled as GON according to the criteria of retinal nerve fibre layer (RNFL) thinning, with a structural defect that correlated in position with the visual field defect (i.e., reference standard). MF were graded by the SDOCT en face images, defined as presence of peripapillary atrophy (PPA), optic disc tilting, or fundus tessellation. The multi-task DL model was developed by ResNet with output of Yes/No GON and Yes/No MF. SDOCT scans were collected in a tertiary eye hospital (Hong Kong SAR, China) for training (80%), tuning (10%), and internal validation (10%). External testing was performed on five independent datasets from eye centres in Hong Kong, the United States, and Singapore, respectively. For GON detection, we compared the model to the average RNFL thickness measurement generated from the SDOCT device. To investigate whether MF can affect the model’s performance on GON detection, we conducted subgroup analyses in groups stratified by Yes/No MF. The area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and accuracy were reported.

Results

A total of 8,151 SDOCT volumetric scans from 3,609 eyes were collected. For detecting GON, in the internal validation, the proposed 3D model had significantly higher AUROC (0.949 vs. 0.913, p &lt; 0.001) than average RNFL thickness in discriminating GON from normal. In the external testing, the two approaches had comparable performance. In the subgroup analysis, the multi-task DL model performed significantly better in the group of “no MF” (0.883 vs. 0.965, p-value &lt; 0.001) in one external testing dataset, but no significant difference in internal validation and other external testing datasets. The multi-task DL model’s performance to detect MF was also generalizable in all datasets, with the AUROC values ranging from 0.855 to 0.896.

Conclusion

The proposed multi-task 3D DL model demonstrated high generalizability in all the datasets and the presence of MF did not affect the accuracy of GON detection generally.

An Artificial-Intelligence-Based Automated Grading and Lesions Segmentation System for Myopic Maculopathy Based on Color Fundus Photographs

Purpose

To develop deep learning models based on color fundus photographs that can automatically grade myopic maculopathy, diagnose pathologic myopia, and identify and segment myopia-related lesions.

Methods

Photographs were graded and annotated by four ophthalmologists and were then divided into a high-consistency subgroup or a low-consistency subgroup according to the consistency between the results of the graders. ResNet-50 network was used to develop the classification model, and DeepLabv3+ network was used to develop the segmentation model for lesion identification. The two models were then combined to develop the classification-and-segmentation–based co-decision model.

Results

This study included 1395 color fundus photographs from 895 patients. The grading accuracy of the co-decision model was 0.9370, and the quadratic-weighted κ coefficient was 0.9651; the co-decision model achieved an area under the receiver operating characteristic curve of 0.9980 in diagnosing pathologic myopia. The photograph-level F₁ values of the segmentation model identifying optic disc, peripapillary atrophy, diffuse atrophy, patchy atrophy, and macular atrophy were all >0.95; the pixel-level F₁ values for segmenting optic disc and peripapillary atrophy were both >0.9; the pixel-level F₁ values for segmenting diffuse atrophy, patchy atrophy, and macular atrophy were all >0.8; and the photograph-level recall/sensitivity for detecting lacquer cracks was 0.9230.

Conclusions

The models could accurately and automatically grade myopic maculopathy, diagnose pathologic myopia, and identify and monitor progression of the lesions.

Translational Relevance

The models can potentially help with the diagnosis, screening, and follow-up for pathologic myopic in clinical practice.

Progression and associated factors of lacquer cracks/patchy atrophies in high myopia: the Beijing Eye Study 2001-2011

PURPOSE

To assess the development and progression of lacquer cracks/patchy atrophies (LCs/PAs) in high myopia.

METHODS

The case control study included highly myopic eyes (refractive error ≤  - 6.0 diopters), examined in the population-based Beijing Eye Study 2001/2011. Using fundus photographs taken in 2001 and 2011 and optical coherence tomographic images obtained in 2011, we assessed the incidence and enlargement of pre-existing LC/PAs.

RESULTS

The study included 89 highly myopic eyes (age: 65.0 ± 9.4 years). Newly developed or enlarged LC/PAs were detected in 17 (19.1%; 95% confidence interval (CI): 11.0, 27.0) eyes, with a new LC development without previous LCs, enlargement of a pre-existing LC, LC enlargement to a PA, development of a new PA without any previous LCs, and enlargement of a pre-existing PA detected in 3, 3, 5, 3, and 3 eyes, respectively. In 14 (82.4%; 95%CI: 62.3, 100) of the 17 eyes with LC/PA development or enlargement, the LC/PAs elongated perpendicularly to, and widened in, the direction of gamma zone enlargement. Higher prevalence of LC/PA enlargement was associated (multivariable analysis) with higher myopic maculopathy stage in 2001 (odds ratio (OR): 7.83; 95%CI: 2.65, 23.2; P < 0.001) and higher frequency of parapapillary delta zone enlargement (OR: 32.0; 95%CI: 3.07, 334; P < 0.001). Prevalence of LC/PA enlargement was lower than the prevalence of changes in other myopic maculopathy features (disc-fovea distance elongation: 71%; choroidal vessel shift: 55%; reduction in ophthalmoscopical disc size: 34%; ophthalmoscopic disc size enlargement: 25%). All eyes with LC/PA enlargement showed a pre-existing and enlarging gamma zone.

CONCLUSIONS

Development and enlargement of LC/PAs were associated with enlargement of parapapillary delta zone and often occurred in association with the direction of gamma zone enlargement.

Prediction of the Fundus Tessellation Severity With Machine Learning Methods

Purpose

To predict the fundus tessellation (FT) severity with machine learning methods.

Methods

A population-based cross-sectional study with 3,468 individuals (mean age of 64.6 ± 9.8 years) based on Beijing Eye Study 2011. Participants underwent detailed ophthalmic examinations including fundus images. Five machine learning methods including ordinal logistic regression, ordinal probit regression, ordinal log-gamma regression, ordinal forest and neural network were used.

Main Outcome Measure

FT precision, recall, F1-score, weighted-average F1-score and AUC value.

Results

Observed from the in-sample fitting performance, the optimal model was ordinal forest, which had correct classification rate (precision) of 81.28%, while 34.75, 93.73, 70.03, and 24.82% in each classified group by FT severity. The AUC value was 0.7249. And the F1-score was 65.05%, weighted-average F1-score was 79.64% on the whole dataset. For out-of-sample prediction performance, the optimal model was ordinal logistic regression, which had precision of 77.12% on the validation dataset, while 19.57, 92.68, 64.74, and 6.76% in each classified group by FT severity. The AUC value was 0.7187. The classification accuracy of light FT group was the highest, while that of severe FT group was the lowest. And the F1-score was 54.46%, weighted-average F1-score was 74.19% on the whole dataset.

Conclusions

The ordinal forest and ordinal logistic regression model had the strong prediction in-sample and out-sample performance, respectively. The threshold ranges of the ordinal forest model for no FT and light, moderate, severe FT were [0, 0.3078], [0.3078, 0.3347], [0.3347, 0.4048], [0.4048, 1], respectively. Likewise, the threshold ranges of ordinal logistic regression model were ≤ 3.7389, [3.7389, 10.5053], [10.5053, 13.9323], > 13.9323. These results can be applied to guide clinical fundus disease screening and FT severity assessment.

Prevalence of fundus tessellation and its associated factors in Chinese children and adolescents with high myopia

Purpose

To investigate the prevalence and associated factors of fundus tessellation in highly myopic children and adolescents.

Methods

A total of 513 high myopes (spherical equivalent [SE] ≤ −5.0 D, 4–19 years of age) without any advanced pathological myopic lesions were enrolled. Fundus photographs and choroidal thickness (ChT) data were collected by SS‐OCT. A novel grading approach was adopted to classify fundus tessellation into four categories on colour fundus photography, referring to the location of tessellation divided by an Early Treatment Diabetic Retinopathy Study grid centred on the fovea, through which closer to the fovea represents higher grades of fundus tessellation. Peripapillary atrophy (PPA) area and ovality index were also measured.

Results

Among the participants, with a mean age of 13.47 ± 3.13 years and mean SE of − 8.34 ± 1.91 D, there were 29 (5.7%), 95 (18.5%), 233 (45.4%) and 156 (30.4%) participants with grade 0 to grade 3 fundus tessellation, respectively. The ChT in both the macular and peripapillary area was negatively correlated with the fundus tessellation grade (R = −0.763 and −0.537, respectively, all p < 0.001). Higher grades of fundus tessellation were independently associated with thinner macular ChT (OR = 1.734, 95% CI: 1.621–1.856, p < 0.001), longer axial length (OR = 1.368, 95% CI: 1.105–1.695, p = 0.004), larger PPA area (OR = 1.391, 95% CI: 1.073–1.802, p = 0.013) and the female sex (OR = 1.605, 95% CI: 1.092–2.359, p = 0.016).

Conclusion

The fundus tessellation grade could reflect the ChT, representing the severity of myopic maculopathy among young high myopes who rarely had any advanced lesions of pathological myopia. Fundus tessellation grade might be a potential index for assessing early‐stage myopic maculopathy in children and adolescents.

Biomarkers for Nonexudative Age-Related Macular Degeneration and Relevance for Clinical Trials: A Systematic Review

TOPIC

The purpose of the review was to identify structural, functional, blood-based, and other types of biomarkers for early, intermediate, and late nonexudative stages of age-related macular degeneration (AMD) and summarize the relevant data for proof-of-concept clinical trials.

CLINICAL RELEVANCE

AMD is a leading cause of blindness in the aging population, yet no treatments exist for its most common nonexudative form. There are limited data on the diagnosis and progression of nonexudative AMD compared to neovascular AMD. Our objective was to provide a comprehensive, systematic review of recently published biomarkers (molecular, structural, and functional) for early AMD, intermediate AMD, and geographic atrophy and to evaluate the relevance of these biomarkers for use in future clinical trials.

METHODS

A literature search of PubMed, ScienceDirect, EMBASE, and Web of Science from January 1, 1996 to November 30, 2020 and a patent search were conducted. Search terms included "early AMD," "dry AMD," "intermediate AMD," "biomarkers for nonexudative AMD," "fundus autofluorescence patterns," "color fundus photography," "dark adaptation," and "microperimetry." Articles were assessed for bias and quality with the Mixed-Methods Appraisal Tool. A total of 94 articles were included (61,842 individuals).

RESULTS

Spectral-domain optical coherence tomography was superior at highlighting detailed structural changes in earlier stages of AMD. Fundus autofluorescence patterns were found to be most important in estimating progression of geographic atrophy. Delayed rod intercept time on dark adaptation was the most widely recommended surrogate functional endpoint for early AMD, while retinal sensitivity on microperimetry was most relevant for intermediate AMD. Combinational studies accounting for various patient characteristics and machine/deep-learning approaches were best suited for assessing individualized risk of AMD onset and progression.

CONCLUSION

This systematic review supports the use of structural and functional biomarkers in early AMD and intermediate AMD, which are more reproducible and less invasive than the other classes of biomarkers described. The use of deep learning and combinational algorithms will gain increasing importance in future clinical trials of nonexudative AMD.

Quantitative Assessment of Fundus Tessellated Density and Associated Factors in Fundus Images Using Artificial Intelligence

Purpose

This study aimed to quantitative assess the fundus tessellated density (FTD) and associated factors on the basis of fundus photographs using artificial intelligence.

Methods

A detailed examination of 3468 individuals was performed. The proposed method for FTD measurements consists of image preprocessing, sample labeling, deep learning segmentation model, and FTD calculation. Fundus tessellation was extracted as region of interest and then the FTD could be obtained by calculating the average exposed choroid area of per unit area of fundus. Besides, univariate and multivariate linear regression analysis have been conducted for the statistical analysis.

Results

The mean FTD was 0.14 ± 0.08 (median, 0.13; range, 0–0.39). In multivariate analysis, FTD was significantly (P < 0.001) associated with thinner subfoveal choroidal thickness, longer axial length, larger parapapillary atrophy, older age, male sex and lower body mass index. Correlation analysis suggested that the FTD increased by 33.1% (r = 0.33, P < .001) for each decade of life. Besides, correlation analysis indicated the negative correlation between FTD and spherical equivalent (SE) in the myopia participants (r = −0.25, P < 0.001), and no correlations between FTD and SE in the hypermetropia and emmetropic participants.

Conclusions

It is feasible and efficient to extract FTD information from fundus images by artificial intelligence–based imaging processing. FTD can be widely used in population screening as a new quantitative biomarker for the thickness of the subfoveal choroid. The association between FTD with pathological myopia and lower visual acuity warrants further investigation.

Translational Relevance

Artificial intelligence can extract valuable clinical biomarkers from fundus images and assist in population screening.

Neural Network-Based Retinal Nerve Fiber Layer Profile Compensation for Glaucoma Diagnosis in Myopia: Model Development and Validation

Background

Due to the axial elongation–associated changes in the optic nerve and retina in high myopia, traditional methods like optic disc evaluation and visual field are not able to correctly differentiate glaucomatous lesions. It has been clinically challenging to detect glaucoma in highly myopic eyes.

Objective

This study aimed to develop a neural network to adjust for the dependence of the peripapillary retinal nerve fiber layer (RNFL) thickness (RNFLT) profile on age, gender, and ocular biometric parameters and to evaluate the network’s performance for glaucoma diagnosis, especially in high myopia.

Methods

RNFLT with 768 points on the circumferential 3.4-mm scan was measured using spectral-domain optical coherence tomography. A fully connected network and a radial basis function network were trained for vertical (scaling) and horizontal (shift) transformation of the RNFLT profile with adjustment for age, axial length (AL), disc-fovea angle, and distance in a test group of 2223 nonglaucomatous eyes. The performance of RNFLT compensation was evaluated in an independent group of 254 glaucoma patients and 254 nonglaucomatous participants.

Results

By applying the RNFL compensation algorithm, the area under the receiver operating characteristic curve for detecting glaucoma increased from 0.70 to 0.84, from 0.75 to 0.89, from 0.77 to 0.89, and from 0.78 to 0.87 for eyes in the highest 10% percentile subgroup of the AL distribution (mean 26.0, SD 0.9 mm), highest 20% percentile subgroup of the AL distribution (mean 25.3, SD 1.0 mm), highest 30% percentile subgroup of the AL distribution (mean 24.9, SD 1.0 mm), and any AL (mean 23.5, SD 1.2 mm), respectively, in comparison with unadjusted RNFLT. The difference between uncompensated and compensated RNFLT values increased with longer axial length, with enlargement of 19.8%, 18.9%, 16.2%, and 11.3% in the highest 10% percentile subgroup, highest 20% percentile subgroup, highest 30% percentile subgroup, and all eyes, respectively.

Conclusions

In a population-based study sample, an algorithm-based adjustment for age, gender, and ocular biometric parameters improved the diagnostic precision of the RNFLT profile for glaucoma detection particularly in myopic and highly myopic eyes.

MOSAICISM AS A PROPOSED MECHANISM FOR ASYMMETRIC RETINAL TESSELLATIONS

BACKGROUND/PURPOSE

Report a case of markedly asymmetric retinal tessellations and propose mosaicism as a mechanism.

METHODS AND RESULTS

A 59-year-old pseudophakic woman presented with uncorrected 20/20 vision and was found to have markedly different retinal tessellation appearances in both eyes. The axial lengths were 25.66 mm and 25.88 mm in the right and left eyes, respectively, and no significant asymmetrical choroidal thinning was seen on optical coherence tomography or optical coherence tomography angiography. Fluorescein angiogram showed significant hyperfluorescence, representing the underlying choroid, which correlated with the tessellation patterns in the left eye. She had no other ocular or systemic findings such as stripes or whorled skin.

CONCLUSION

This is the first reported case of markedly asymmetric retinal tessellation patterns that are not due to asymmetric axial myopia or choroidal thinning. We propose that mosaicism is a possible mechanism causing this finding.

Characteristics of Fundal Changes in Fundus Tessellation in Young Adults

Purpose: To explore the characteristics and associated factors of fundus tessellation, especially the alternation of choroidal thickness among different degrees of tessellated fundus in young adults.

Design: Cross-sectional, population-based study.

Methods: A total of 796 students were included in the study and underwent comprehensive ophthalmic examinations, including anterior segment examinations and swept-source optical coherence tomography (OCT) measurements. The degree of tessellated fundus was assessed by fundus photographs applying an early treatment of diabetic retinopathy study grid to evaluate the location of fundus tessellation and then divided into five groups. The topographic variation and factors, tilted disc ratio, parapapillary atrophy (PPA), retinal thickness (ReT), choroidal thickness (ChT), and subfoveal scleral thickness (SST) related to tessellated fundus were analyzed.

Results: Compared to normal fundus, tessellated fundus had a lower spherical equivalent (SE) (p < 0.0001), worse best-corrected visual acuity (BCVA)(p = 0.043), longer axial length (AL) (p < 0.0001), thinner retina (p < 0.0001), thinner (p < 0.0001) choroid, and thinner sclera in center fovea (p = 0.0035). Among all subfields of macular and peripapillary regions, center fovea and macula-papillary region showed the most significant decrease in choroidal thickness. The proportion of fundus tessellation significantly increased with lower body weight index (BMI) (p = 0.0067), longer AL (p < 0.0001), larger PPA(p = 0.0058), thinner choroid (p < 0.0001), and thinner sclera (p < 0.0001).

Conclusions: Eyes showed more severe myopic morphological alternation with the increasement of proportion of fundus tessellation to the center fovea, including a significant decrease in both choroid and scleral thickness. Choroidal thinning may progress most rapidly in the macula-papillary region as fundus tessellation approaches to the center fovea.

Deep Learning-Based Estimation of Axial Length and Subfoveal Choroidal Thickness From Color Fundus Photographs

This study aimed to develop an automated computer-based algorithm to estimate axial length and subfoveal choroidal thickness (SFCT) based on color fundus photographs. In the population-based Beijing Eye Study 2011, we took fundus photographs and measured SFCT by optical coherence tomography (OCT) and axial length by optical low-coherence reflectometry. Using 6394 color fundus images taken from 3468 participants, we trained and evaluated a deep-learning-based algorithm for estimation of axial length and SFCT. The algorithm had a mean absolute error (MAE) for estimating axial length and SFCT of 0.56 mm [95% confidence interval (CI): 0.53,0.61] and 49.20 μm (95% CI: 45.83,52.54), respectively. Estimated values and measured data showed coefficients of determination of r 2 = 0.59 (95% CI: 0.50,0.65) for axial length and r 2 = 0.62 (95% CI: 0.57,0.67) for SFCT. Bland-Altman plots revealed a mean difference in axial length and SFCT of -0.16 mm (95% CI: -1.60,1.27 mm) and of -4.40 μm (95% CI, -131.8,122.9 μm), respectively. For the estimation of axial length, heat map analysis showed that signals predominantly from overall of the macular region, the foveal region, and the extrafoveal region were used in the eyes with an axial length of < 22 mm, 22-26 mm, and > 26 mm, respectively. For the estimation of SFCT, the convolutional neural network (CNN) used mostly the central part of the macular region, the fovea or perifovea, independently of the SFCT. Our study shows that deep-learning-based algorithms may be helpful in estimating axial length and SFCT based on conventional color fundus images. They may be a further step in the semiautomatic assessment of the eye.

Prevalence and associated factors of glaucoma in the Russian Ural Eye and Medical Study

To assess the prevalence and associated factors of glaucoma in a Russian population. The population-based Ural Eye and Medical Study included 5899 (mean age 59.0 ± 10.7 years; range 40–94 years). Glaucomatous optic neuropathy was diagnosed using International Society of Geographical and Epidemiological Ophthalmology (ISGEO) criteria. Among 5545 participants with assessable optic disc photographs, 246 individuals [4.4%; 95% confidence interval (CI) 3.9, 5.0] had glaucoma, with open-angle glaucoma (OAG) in 177 individuals (3.2%; 95% CI 2.7, 3.7) and angle-closure glaucoma (ACG) in 69 individuals (1.2; 95% CI 1.0, 1.5), with IOP > 21 mmHg in 79 (32.1%) patients, and with 80 (32.5%) patients on glaucoma therapy. Glaucoma prevalence increased from 3/485 (0.6%; 95% CI 0.0, 1.3) in the age group of 40–45 years to 33/165 (20.0%; 95% CI 13.8, 26.2) in the group aged 80 + years. Higher OAG prevalence correlated with older age [odds ratio (OR) 1.07; 95% CI 1.04, 1.09; P < 0.001], longer axial length (OR 1.36; 95% CI 1.17, 1.58; P < 0.001), higher intraocular pressure (IOP) (OR 1.18; 95% CI 1.13, 1.23; P < 0.001), higher stage of lens pseudoexfoliation (OR 1.26; 95% CI 1.08, 1.47; P = 0.004) and lower diastolic blood pressure (OR 0.98; 95% CI 0.96, 0.99; P = 0.035). Higher ACG prevalence correlated with older age (OR 1.07; 95% CI 1.03, 1.11; P < 0.001), narrower anterior chamber angle (OR 0.81; 95% CI 0.77, 0.86; P < 0.001), and higher IOP (OR 1.30; 95% CI 1.23, 1.38; P < 0.001). Glaucoma caused moderate to severe vision impairment (MSVI) in 9 (4.9%; 95% CI 1.8, 8.1) out of 184 individuals with MSVI (OAG, n = 7; ACG, n = 2), and blindness in one (9.1%) of 11 blind individuals. In this population from Russia, two thirds of glaucoma patients were not on therapy, and in two thirds of the glaucoma patients IOP was ≤ 21 mmHg. Otherwise, glaucoma prevalence, OAG-to-ACG ratio, and glaucoma associations did not differ markedly from Caucasian and East Asian populations.

Prevalence and associated factors of cataract and cataract-related blindness in the Russian Ural Eye and Medical Study

To assess the prevalence of cataract and cataract surgery in a population from Russia, we conducted the population-based Ural Eye and Medical Study with 5899 participants (80.5% out of 7328 eligible individuals), with an age of 40 + years as the eligibility criterion. In the phakic population, the prevalence of nuclear, cortical, subcapsular cataract and any cataract was 38.0% [95% confidence interval (CI) 36.6, 39.3], 14.5% (95% CI 13.5, 15.5), 0.6% (95% CI 0.4, 0.8) and 44.6% (95% CI 43.2, 46.0), respectively. A higher prevalence of nuclear cataract was associated with older age [odds ratio (OR) 1.10; 95% CI 1.10, 1.11], the female sex (OR 1.27; 95% CI 1.08, 1.50), urban region (OR 2.00; 95% CI 1.71, 2.33), a low educational level (OR 0.93; 95% CI 0.88, 0.98), a high diastolic blood pressure (OR 1.01; 95% CI 1.001, 1.02), a low serum concentration of high-density lipoproteins (OR 0.91; 95% CI 0.84, 0.98), more smoking package years (OR 1.01; 95% CI 1.01, 1.02), chronic kidney disease (OR 1.02; 95% CI 1.10, 1.03), a short axial length (OR 0.93; 95% CI 0.86, 0.99), and a low prevalence of age-related macular degeneration (OR 0.72; 95% CI 0.57, 0.92). The prevalence of previous cataract surgery conducted in 354/5885 individuals (6.0%; 95% CI 5.4, 6.6) increased from 0.4% (95% CI 0.0, 1.0) in the age group of 40-45 years to 37.6% (95% CI 30.9, 44.4) in the age group of 80 + years. Cataract was the cause of moderate-to-severe vision impairment in 109 (1.8%) individuals and of blindness in three (0.05%) individuals. The prevalence of cataract and cataract-related MSVI and blindness were relatively high; subsequently, the prevalence of previous cataract surgery was relatively low in this population from Russia.

Sex judgment using color fundus parameters in elementary school students

PURPOSES

Recently, artificial intelligence has been used to determine sex using fundus photographs alone. We had earlier reported that sex can be distinguished using known factors obtained from color fundus photography (CFP) in adult eyes. However, it is not clear when the sex difference in fundus parameters begins. Therefore, we conducted this study to investigate sex determination based on fundus parameters using binominal logistic regression in elementary school students.

METHODS

This prospective observational cross-sectional study was conducted on 119 right eyes of elementary school students (aged 8 or 9 years, 59 boys and 60 girls). Through CFP, the tessellation fundus index was calculated as R/(R + G + B) using the mean value of red-green-blue intensity in the eight locations around the optic disc. Optic disc ovality ratio, papillomacular angle, retinal artery trajectory, and retinal vessel were quantified based on our earlier reports. Regularized binomial logistic regression was applied to these variables to select the decisive factors. Furthermore, its discriminative performance was evaluated using the leave-one-out cross-validation method. Sex difference in the parameters was assessed using the Mann-Whitney U test.

RESULTS

The optimal model yielded by the Ridge binomial logistic regression suggested that the ovality ratio of girls was significantly smaller, whereas their nasal green and blue intensities were significantly higher, than those of boys. Using this approach, the area under the receiver-operating characteristic curve was 63.2%.

CONCLUSIONS

Although sex can be distinguished using CFP even in elementary school students, the discrimination accuracy was relatively low. Some sex difference in the ocular fundus may begin after the age of 10 years.

Prevalence, risk factors and associated ocular diseases of cerebral stroke: the population-based Beijing Eye Study

OBJECTIVE

To assess the prevalence of cerebral stroke in the general population of Beijing and its association with systemic risk factors and ocular diseases.

SETTING

The population-based Beijing Eye Study was conducted in a rural and urban region of Beijing.

PARTICIPANTS

With eligibility criteria of age 50+ years and living in the study regions, 3468 subjects (78.8%) out of 4403 eligible individuals participated.

PRIMARY AND SECONDARY OUTCOME MEASURES

The study participants underwent a detailed systemic and ophthalmological examination and an interview in which the occurrence of a previous stroke was assessed.

RESULTS

A previous stroke was reported by 235 individuals (7.33%; 95% CI 6.43% to 8.24%). The prevalence of previous stroke increased from 2.0% (95% CI 0.9% to 3.1%) in the age group of 50 to <55 years to 21.9% (95% CI 16.4% to 27.4%) in the age group of 80+ years. In multivariable regression analysis, a higher prevalence of previous stroke was correlated (Nagelkerke R²=0.20) with the systemic parameters of older age (p<0.001; OR 1.06; 95% CI 1.04 to 1.08), male gender (p<0.001; OR 0.54; 95% CI 0.40 to 0.74), lower quality of life score (p<0.001; OR 1.39; 95% CI 1.25 to 1.55), higher prevalence of arterial hypertension (p<0.001; OR 2.86; 95% CI 2.05 to 3.98), and cardiovascular disease (p<0.001; OR 1.8554; 95% CI 1.34 to 2.56), and with the ocular parameter of higher prevalence of diabetic retinopathy (p<0.001; OR 4.41; 95% CI 2.38 to 8.18) or alternatively, with higher stage of diabetic retinopathy (p<0.001; OR 1.64; 95% CI 1.26 to 2.14).

CONCLUSIONS

In this North Chinese population aged 50+ years, the prevalence of a previous stroke was 7.33% (95% CI 6.43% to 8.24%). After adjusting for systemic risk factors of older age, male gender and higher prevalence of arterial hypertension and cardiovascular disease, a higher prevalence of a previous stroke was significantly correlated with a higher prevalence and stage of diabetic retinopathy. The prevalence of a previous stroke increased for each step of an increase in the stage of diabetic retinopathy with an OR of 1.64 (95% CI 1.26 to 2.14), and it increased by the presence of diabetic retinopathy with an OR of 4.41 (95% CI 2.38 to 8.18).

Prevalence and predictors of myopic macular degeneration among Asian adults: pooled analysis from the Asian Eye Epidemiology Consortium

AIMS

To determine the prevalence and predictors of myopic macular degeneration (MMD) in a consortium of Asian studies.

METHODS

Individual-level data from 19 885 participants from four population-based studies, and 1379 highly myopic participants (defined as axial length (AL) >26.0 mm) from three clinic-based/school-based studies of the Asian Eye Epidemiology Consortium were pooled. MMD was graded from fundus photographs following the meta-analysis for pathologic myopia classification and defined as the presence of diffuse choroidal atrophy, patchy chorioretinal atrophy, macular atrophy, with or without 'plus' lesion (lacquer crack, choroidal neovascularisation or Fuchs' spot). Area under the curve (AUC) evaluation for predictors was performed for the population-based studies.

RESULTS

The prevalence of MMD was 0.4%, 0.5%, 1.5% and 5.2% among Asians in rural India, Beijing, Russia and Singapore, respectively. In the population-based studies, older age (per year; OR=1.13), female (OR=2.0), spherical equivalent (SE; per negative diopter; OR=1.7), longer AL (per mm; OR=3.1) and lower education (OR=1.9) were associated with MMD after multivariable adjustment (all p<0.001). Similarly, in the clinic-based/school-based studies, older age (OR=1.07; p<0.001), female (OR=2.1; p<0.001), longer AL (OR=2.1; p<0.001) and lower education (OR=1.7; p=0.005) were associated with MMD after multivariable adjustment. SE had the highest AUC of 0.92, followed by AL (AUC=0.87). The combination of SE, age, education and gender had a marginally higher AUC (0.94).

CONCLUSION

In this pooled analysis of multiple Asian studies, older age, female, lower education, greater myopia severity and longer AL were risk factors of MMD, and myopic SE was the strongest single predictor of MMD.

Prevalence and causes of vision impairment and blindness in the Russian ural eye and medical study

To assess prevalence of mild vision impairment (MVI; best corrected visual acuity (BCVA) < 6/12 to 6/18 in the better eye), moderate-to-severe vision impairment (MSVI; BCVA < 6/18 but ≥ 3/60) and blindness (BCVA < 3/60) in a local population in Russia, we conducted the population-based Ural Eye and Medical Study. Out of 7,328 eligible individuals aged 40 + years, 5,899 (80.5%) individuals participated. MVI was present in 184 (3.1%; 95% confidence interval (CI) 2.7, 3.6) individuals, MSVI in 182 (3.1%; 95% CI 2.7, 3.5) individuals, and 11 individuals (0.19%; 95% CI 0.008, 0.30) were blind. Causes for MSVI were cataract (n = 109; 59.9%), late stage of age-related macular degeneration (n = 14; 7.7%; geographic atrophy and neovascular AMD in 7 (3.8%) individuals) each), myopic maculopathy (n = 11; 6.0%), glaucoma (n = 9; 4.9%), non-glaucomatous optic nerve damage (n = 5; 2.7%), and diabetic retinopathy (n = 4; 2.2%). Causes for blindness were cataract (n = 3; 27.3%), myopic maculopathy (n = 2; 18.2%), retinal dystrophies (n = 2; 18.2%), glaucoma (n = 1; 9.1%), and corneal scars (n = 1; 9.1%). Higher prevalence of MSVI/blindness was associated with age (P < 0.001; odds ratio (OR)1.10; 95% CI 1.08, 1.12), male gender (P < 0.001; OR 2.32; 95% CI 1.47, 3.66), educational level (P < 0.001; OR 0.83; 95% CI 0.76,0.92), manual grip force (P < 0.001; OR 0.94; 95% CI 0.92, 0.96), diabetes prevalence (P = 0.006; OR 1.67; 95% CI 1.08, 2.56) and axial length (P < 0.001; OR 1.43; 95% CI 1.26,1.62). In this population from Bashkortostan/Russia, prevalence of MVI, MSVI and blindness was 3.1%, 3.1% and 0.19%, respectively. Cataract was the most frequent cause of reversible vision impairment, while AMD, myopic maculopathy and glaucoma were the most common reasons for irreversible vision impairment.

Refractive Errors, Ocular Biometry and Diabetic Retinopathy: A Comprehensive Review

Purpose: To summarize the association between diabetic retinopathy and refractory status as well as ocular biometric parameters; To review the theories of the protective effect of high myopia against diabetic retinopathy. Methods: A comprehensive literature search on MEDLINE, EMBASE, Web of Science and Scopus databases as well as reference list search, and systematic review of relevant publications.  Results: Myopia may delay the onset and progression of diabetic retinopathy. Increased axial length in myopia is associated with reduced risk of any diabetic retinopathy and vision-threatening diabetic retinopathy. The possible mechanisms for the protective effect of myopia against diabetic retinopathy may include posterior vitreous detachment, change in retinal blood flow and oxygen demand, choroidal thinning and altered cytokine profiles.  Conclusions: High myopia may be a protective factor against the onset and progression of diabetic retinopathy. Further studies about the mechanisms of how myopia, axial length and ocular biometrics influence the onset and progression of DR are needed.

Illustration of tessellation in Down syndrome

Background: Tessellated fundus refers to a specific change in the appearance of the internal layers of the eye in which the choroidal large vessels became visible through polygonal hypopigmented areas. Such hypopigmented areas alternate with hyperpigmented zones in a tigroid pattern. Fundus tessellation is often associated with myopia and choroidal thinning.Materials and Methods: We analyzed fundus images from 50 children with Down syndrome and 52 controls.Results: Tessellation was present in 64% of children with Down syndrome, compared with only 13.5% of controls (p < .0001). In most cases, tessellation was located peripapillary, and no difference was observed in tessellation localization between children with Down syndrome and controls (p = .60). Although more prevalent in myopic children with and without Down syndrome, tessellation was present in almost half (48%) of children with Down syndrome with hyperopia versus only 5% of controls with the same refractive status.Conclusions: Mechanical stretching of the choroid could explain the high rate of tessellation in myopes. Other factors must contribute to the higher prevalence of tessellated fundus in children with Down syndrome without myopia. We discuss potentially relevant factors and propose vascular involvement as a contributor to tessellation in our population with Down syndrome. Further studies assessing choroidal vasculature in individuals with Down syndrome are needed to confirm this theory.

Underweight increases the risk of primary open-angle glaucoma in diabetes patients: A Korean nationwide cohort study

The impact of underweight on the risk of developing primary open-angle glaucoma (POAG) is not known, although the association between obesity and POAG has been well studied. We evaluated the risk of POAG among underweight patients by studying a nationwide cohort sample in South Korea.We analyzed data from the Korean National Health Insurance Research Database collected between 2009 and 2012 for 17,000,636 patients aged 40 years or older. Newly diagnosed POAG in the cohort was identified using claims data between 2009 and 2015.A total of 442,829 individuals (2.60%) were classified as underweight (body mass index [BMI] < 18.5 kg/m). During the follow-up period, 435,756 (2.56%) subjects were newly diagnosed with POAG. Multivariate analyses revealed that underweight was significantly related to an increased risk of future POAG development, by 9.8% and 27.8% in individuals with and without diabetes, respectively. There was a reverse J-shaped relationship between BMI and risk of POAG in the normal, impaired glucose tolerance, and diabetes groups; especially, this relationship was most notable in participants with diabetes.Patients who were underweight exhibited a significantly higher prospective risk of POAG, even after adjusting for confounding factors.