Assessment of angle closure disease in the age of artificial intelligence: A review.

Primary angle closure glaucoma is a visually debilitating disease that is under-detected worldwide. Many of the challenges in managing primary angle closure disease (PACD) are related to the lack of convenient and precise tools for clinic-based disease assessment and monitoring. Artificial intelligence (AI)- assisted tools to detect and assess PACD have proliferated in recent years with encouraging results. Machine learning (ML) algorithms that utilize clinical data have been developed to categorize angle closure eyes by disease mechanism. Other ML algorithms that utilize image data have demonstrated good performance in detecting angle closure. Nonetheless, deep learning (DL) algorithms trained directly on image data generally outperformed traditional ML algorithms in detecting PACD, were able to accurately differentiate between angle status (open, narrow, closed), and automated the measurement of quantitative parameters. However, more work is required to expand the capabilities of these AI algorithms and for deployment into real-world practice settings. This includes the need for real-world evaluation, establishing the use case for different algorithms, and evaluating the feasibility of deployment while considering other clinical, economic, social, and policy-related factors.

Six-Year Incidence of Visual Impairment in a Multiethnic Asian Population: The Singapore Epidemiology of Eye Diseases Study.

Purpose: To examine the 6-year incidence of visual impairment (VI) and identify risk factors associated with VI in a multiethnic Asian population. Design: Prospective, population-based, cohort study. Participants: Adults aged ≥ 40 years were recruited from the Singapore Epidemiology of Eye Diseases cohort study at baseline. Eligible subjects were re-examined after 6 years. Subjects included in the final analysis had a mean age of 56.1 ± 8.9 years, and 2801 (50.5%) were female. Methods: All participants underwent standardized examination and interviewer-administered questionnaire at baseline. Incidences were standardized to the Singapore Population Census 2010. A Poisson binomial regression model was used to evaluate the associations between baseline factors and incident presenting VI. Main Outcome Measures: Incident presenting VI was assessed at the 6-year follow-up visit. Visual impairment (presenting visual acuity < 20/40), low vision (presenting visual acuity < 20/40 but ≥ 20/200), and blindness (presenting visual acuity < 20/200) were defined based on United States definition. Results: A total of 5551 subjects (2188 Chinese, 1837 Indians, and 1526 Malays) were evaluated, of whom 514 developed incident presenting VI over 6 years. Malays had a higher incidence of low vision and blindness (13.0%; 0.6%) than Indians (7.0%; 0.1%) and Chinese (7.7%; 0.2%). Among Malay individuals with VI at baseline, 52.8% remained visually impaired after 6 years, which was considerably higher than Chinese (32.4%) and Indians (37.2%). Older age (per decade; relative risk [RR] = 1.59), a history of cardiovascular disease (RR = 1.38), current smoking (RR = 1.31), smaller housing type (1- to 2-room public flat; RR = 2.01), and no formal education (RR = 1.63) at baseline were associated with a higher risk of incident VI (all P ≤ 0.027). Older age (> 60 years) contributed the highest population attributable risk to incident VI (27.1%), followed by lower monthly income (Singapore dollar < $2000; 26.4%) and smaller housing type (24.7%). Overall, undercorrected refractive error (49.1%) and cataract (82.6%) were leading causes for low vision and blindness, respectively. This was consistently observed across the 3 ethnicities. Conclusions: In this multiethnic Asian population, Malays had a higher VI incidence compared to Indians and Chinese. Leading causes of VI are mostly treatable, suggesting that more efforts are needed to further mitigate preventable visual loss. Financial Disclosures: The authors have no proprietary or commercial interest in any materials discussed in this article.

Cardiovascular disease and thinning of retinal nerve fiber layer in a multi-ethnic Asian population: the Singapore epidemiology of eye diseases study.

Introduction: Our study aimed to examine the relationship between cardiovascular diseases (CVD) with peripapillary retinal fiber layer (RNFL) and macular ganglion cell-inner plexiform layer (GCIPL) thickness profiles in a large multi-ethnic Asian population study. Methods: 6,024 Asian subjects were analyzed in this study. All participants underwent standardized examinations, including spectral domain OCT imaging (Cirrus HD-OCT; Carl Zeiss Meditec). In total, 9,188 eyes were included for peripapillary RNFL analysis (2,417 Malays; 3,240 Indians; 3,531 Chinese), and 9,270 eyes (2,449 Malays, 3,271 Indians, 3,550 Chinese) for GCIPL analysis. History of CVD was defined as a self-reported clinical history of stroke, myocardial infarction, or angina. Multivariable linear regression models with generalized estimating equations were performed, adjusting for age, gender, ethnicity, diabetes, hypertension, hyperlipidaemia, chronic kidney disease, body mass index, current smoking status, and intraocular pressure. Results: We observed a significant association between CVD history and thinner average RNFL (ß = -1.63; 95% CI, -2.70 to -0.56; p = 0.003). This association was consistent for superior (ß = -1.79, 95% CI, -3.48 to -0.10; p = 0.038) and inferior RNFL quadrant (ß = -2.14, 95% CI, -3.96 to -0.32; p = 0.021). Of the CVD types, myocardial infarction particularly showed significant association with average (ß = -1.75, 95% CI, -3.08 to -0.42; p = 0.010), superior (ß = -2.22, 95% CI, -4.36 to -0.09; p = 0.041) and inferior (ß = -2.42, 95% CI, -4.64 to -0.20; p = 0.033) RNFL thinning. Among ethnic groups, the association between CVD and average RNFL was particularly prominent in Indian eyes (ß = -1.92, 95% CI, -3.52 to -0.33; p = 0.018). CVD was not significantly associated with average GCIPL thickness, albeit a consistent negative direction of association was observed (ß = -0.22, 95% CI, -1.15 to 0.71; p = 0.641). Discussion: In this large multi-ethnic Asian population study, we observed significant association between CVD history and RNFL thinning. This finding further validates the impact of impaired systemic circulation on RNFL thickness.

Correction: From 2 dimensions to 3rd dimension: Quantitative prediction of anterior chamber depth from anterior segment photographs via deep-learning.

[This corrects the article DOI: 10.1371/journal.pdig.0000193.].

Application of big data in ophthalmology.

The advents of information technologies have led to the creation of ever-larger datasets. Also known as big data, these large datasets are characterized by its volume, variety, velocity, veracity, and value. More importantly, big data has the potential to expand traditional research capabilities, inform clinical practice based on real-world data, and improve the health system and service delivery. This review first identified the different sources of big data in ophthalmology, including electronic medical records, data registries, research consortia, administrative databases, and biobanks. Then, we provided an in-depth look at how big data analytics have been applied in ophthalmology for disease surveillance, and evaluation on disease associations, detection, management, and prognostication. Finally, we discussed the challenges involved in big data analytics, such as data suitability and quality, data security, and analytical methodologies.

Assessment of anxiety and depression in patients with Posner-Schlossman syndrome.

BACKGROUND: To assess the anxiety and depression levels in patients with Posner-Schlossman syndrome (PSS) and to determine the potential risk factors. METHODS: In this cross-sectional study, a total of 195 participants, including 93 PSS patients and 102 healthy controls were recruited. Sociodemographic and clinical information were collected for all participants. Hospital Anxiety and Depression scale (HADS) was administered to evaluate the anxiety and depression levels. Visual function (VF) and quality-of-life (QOL) questionnaires were administered to assess variables potentially associated with anxiety and depression. RESULTS: Increased anxiety level was observed in 22 (23.7%) PSS patients as compared to 10 (9.8%) of controls (P = 0.009). While the frequency of depression between the two groups was not significantly different (P = 0.349). The mean anxiety and depression scores were 6.98 ± 4.20 and 6.44 ± 3.66 in PSS patients as compared to 6.67 ± 3.21 (P = 0.564) and 5.96 ± 2.93 (P = 0.311) in controls. Logistic regression analysis showed mental well-being was significantly associated with anxiety (odds ratio [OR] = 0.920, 95% confidence interval [CI] = 0.881-0.962, P < 0.001) and depression (OR = 0.959, CI = 0.926-0.994, P = 0.023) in PSS patients. CONCLUSION: More patients with PSS may experience anxiety as compared to healthy controls. Mental well-being is an independent risk factor for anxiety and depression. It is important for ophthalmologists to be aware of these factors and should pay more attention on mental health when PSS is managed in clinic.

From 2 dimensions to 3rd dimension: Quantitative prediction of anterior chamber depth from anterior segment photographs via deep-learning.

Anterior chamber depth (ACD) is a major risk factor of angle closure disease, and has been used in angle closure screening in various populations. However, ACD is measured from ocular biometer or anterior segment optical coherence tomography (AS-OCT), which are costly and may not be readily available in primary care and community settings. Thus, this proof-of-concept study aims to predict ACD from low-cost anterior segment photographs (ASPs) using deep-learning (DL). We included 2,311 pairs of ASPs and ACD measurements for algorithm development and validation, and 380 pairs for algorithm testing. We captured ASPs with a digital camera mounted on a slit-lamp biomicroscope. Anterior chamber depth was measured with ocular biometer (IOLMaster700 or Lenstar LS9000) in data used for algorithm development and validation, and with AS-OCT (Visante) in data used for testing. The DL algorithm was modified from the ResNet-50 architecture, and assessed using mean absolute error (MAE), coefficient-of-determination (R2), Bland-Altman plot and intraclass correlation coefficients (ICC). In validation, our algorithm predicted ACD with a MAE (standard deviation) of 0.18 (0.14) mm; R2 = 0.63. The MAE of predicted ACD was 0.18 (0.14) mm in eyes with open angles and 0.19 (0.14) mm in eyes with angle closure. The ICC between actual and predicted ACD measurements was 0.81 (95% CI 0.77, 0.84). In testing, our algorithm predicted ACD with a MAE of 0.23 (0.18) mm; R2 = 0.37. Saliency maps highlighted the pupil and its margin as the main structures used in ACD prediction. This study demonstrates the possibility of predicting ACD from ASPs via DL. This algorithm mimics an ocular biometer in making its prediction, and provides a foundation to predict other quantitative measurements that are relevant to angle closure screening.

Machine learning identifying peripheral circulating metabolites associated with intraocular pressure alterations.

AIMS: To identify blood metabolite markers associated with intraocular pressure (IOP) in a population-based cross-sectional study. METHODS: This study was conducted in a multiethnic Asian population (Chinese, n=2805; Indians, n=3045; Malays, n=3041 aged 40-80 years) in Singapore. All subjects underwent standardised systemic and ocular examinations, and biosamples were collected. Selected metabolites (n=228) in either serum or plasma were analysed and quantified using nuclear magnetic resonance spectroscopy. Least absolute shrinkage and selection operator regression was used for metabolites selection. Multivariable linear regression was used to evaluate the relationship between metabolites and IOP in each of the three ethnic groups, followed by a meta-analysis combining the three cohorts. RESULTS: Six metabolites, including albumin, glucose, lactate, glutamine, ratio of saturated fatty acids to total fatty acids (SFAFA) and cholesterol esters in very large high-density lipoprotein (HDL), were significantly associated with IOP in all three cohorts. Higher levels of albumin (per SD, beta=0.24, p=0.002), lactate (per SD, beta=0.27, p=0.008), glucose (per SD, beta=0.11, p=0.010) and cholesterol esters in very large HDL (per SD, beta=0.47, p=0.006), along with lower levels of glutamine (per SD, beta=0.17, p<0.001) and SFAFA (per SD, beta=0.21, p=0.008) were associated with higher IOP levels. CONCLUSION: We identify several novel blood metabolites associated with IOP. These findings may provide insight into the physiological and pathological processes underlying IOP control.

Smartphone-Acquired Anterior Segment Images for Deep Learning Prediction of Anterior Chamber Depth: A Proof-of-Concept Study.

Purpose: To develop a deep learning (DL) algorithm for predicting anterior chamber depth (ACD) from smartphone-acquired anterior segment photographs. Methods: For algorithm development, we included 4,157 eyes from 2,084 Chinese primary school students (aged 11-15 years) from Mojiang Myopia Progression Study (MMPS). All participants had with ACD measurement measured with Lenstar (LS 900) and anterior segment photographs acquired from a smartphone (iPhone Xs), which was mounted on slit lamp and under diffuses lighting. The anterior segment photographs were randomly selected by person into training (80%, no. of eyes = 3,326) and testing (20%, no. of eyes = 831) dataset. We excluded participants with intraocular surgery history or pronounced corneal haze. A convolutional neural network was developed to predict ACD based on these anterior segment photographs. To determine the accuracy of our algorithm, we measured the mean absolute error (MAE) and coefficient of determination (R 2) were evaluated. Bland Altman plot was used to illustrate the agreement between DL-predicted and measured ACD values. Results: In the test set of 831 eyes, the mean measured ACD was 3.06 ± 0.25 mm, and the mean DL-predicted ACD was 3.10 ± 0.20 mm. The MAE was 0.16 ± 0.13 mm, and R 2 was 0.40 between the predicted and measured ACD. The overall mean difference was -0.04 ± 0.20 mm, with 95% limits of agreement ranging between -0.43 and 0.34 mm. The generated saliency maps showed that the algorithm mainly utilized central corneal region (i.e., the site where ACD is clinically measured typically) in making its prediction, providing further plausibility to the algorithm's prediction. Conclusions: We developed a DL algorithm to estimate ACD based on smartphone-acquired anterior segment photographs. Upon further validation, our algorithm may be further refined for use as a ACD screening tool in rural localities where means of assessing ocular biometry is not readily available. This is particularly important in China where the risk of primary angle closure disease is high and often undetected.

Six-Year Incidence and Risk Factors for Primary Angle-Closure Disease: The Singapore Epidemiology of Eye Diseases Study.

PURPOSE: To determine the incidence and risk factors of primary angle-closure disease (PACD) over 6 years in a multi-ethnic Asian population. DESIGN: Population-based, longitudinal study. PARTICIPANTS: The Singapore Epidemiology of Eye Diseases study is a population-based cohort study conducted among adults aged 40 years or more. The baseline examination was conducted between 2004 and 2010, and the 6-year follow-up visit was conducted between 2011 and 2017. Of 6762 participants who attended the follow-up examination, 5298 at risk for primary angle-closure glaucoma (PACG) and 5060 at risk for PACD were included for analyses. METHODS: Standardized examinations including slit-lamp biomicroscopy, indentation gonioscopy, intraocular pressure (IOP) measurement, and static automated perimetry were performed. In this study, PACD includes primary angle-closure suspect (PACS), primary angle-closure (PAC), and PACG. MAIN OUTCOME MEASURES: The 6-year PACD incidence was evaluated among an at-risk population excluding adults with baseline glaucoma, PACS, PAC, pseudophakia at baseline or follow-up, or laser peripheral iridotomy or iridectomy at baseline visit. Logistic regression analysis adjusting for age, gender, and ethnicity was performed to evaluate associations between PACD development and demographic or ocular characteristics. Forward selection based on the Quasi-likelihood Information Criterion was used in multivariable analysis to reduce potential multicollinearity. RESULTS: The 6-year age-adjusted PACD incidence was 3.50% (95% confidence interval [CI], 2.94-4.16). In multivariable analysis, increasing age per decade (odds ratio [OR], 1.35; 95% CI, 1.15-1.59), higher IOP (OR, 1.04; 95% CI, 1.00-1.08), and shallower anterior chamber depth (OR, 1.11; 95% CI, 1.08-1.14) at baseline were associated with higher odds of PACD, whereas late posterior subcapsular cataract (PSC) (OR, 0.60; 95% CI, 0.48-0.76) was associated with lower odds of PACD. The 6-year age-adjusted incidences of PACG, PAC, and PACS were 0.29% (95% CI, 0.14-0.55), 0.46% (95% CI, 0.29-0.75), and 2.54% (95% CI, 2.07-3.12), respectively. CONCLUSIONS: Our study showed that the 6-year incidence of PACD was 3.50%. Increasing age, higher IOP, and shallower anterior chamber were associated with a higher risk of incident PACD, whereas late PSC was associated with a lower odds of PACD. These findings can aid in future projections and formulation of health care policies for screening of at-risk individuals for timely intervention.

Deep learning algorithms for automatic detection of pterygium using anterior segment photographs from slit-lamp and hand-held cameras.

BACKGROUND/AIMS: To evaluate the performances of deep learning (DL) algorithms for detection of presence and extent pterygium, based on colour anterior segment photographs (ASPs) taken from slit-lamp and hand-held cameras. METHODS: Referable pterygium was defined as having extension towards the cornea from the limbus of >2.50 mm or base width at the limbus of >5.00 mm. 2503 images from the Singapore Epidemiology of Eye Diseases (SEED) study were used as the development set. Algorithms were validated on an internal set from the SEED cohort (629 images (55.3% pterygium, 8.4% referable pterygium)), and tested on two external clinic-based sets (set 1 with 2610 images (2.8% pterygium, 0.7% referable pterygium, from slit-lamp ASP); and set 2 with 3701 images, 2.5% pterygium, 0.9% referable pterygium, from hand-held ASP). RESULTS: The algorithm's area under the receiver operating characteristic curve (AUROC) for detection of any pterygium was 99.5%(sensitivity=98.6%; specificity=99.0%) in internal test set, 99.1% (sensitivity=95.9%, specificity=98.5%) in external test set 1 and 99.7% (sensitivity=100.0%; specificity=88.3%) in external test set 2. For referable pterygium, the algorithm's AUROC was 98.5% (sensitivity=94.0%; specificity=95.3%) in internal test set, 99.7% (sensitivity=87.2%; specificity=99.4%) in external set 1 and 99.0% (sensitivity=94.3%; specificity=98.0%) in external set 2. CONCLUSION: DL algorithms based on ASPs can detect presence of and referable-level pterygium with optimal sensitivity and specificity. These algorithms, particularly if used with a handheld camera, may potentially be used as a simple screening tool for detection of referable pterygium. Further validation in community setting is warranted. SYNOPSIS/PRECIS: DL algorithms based on ASPs can detect presence of and referable-level pterygium optimally, and may be used as a simple screening tool for the detection of referable pterygium in community screenings.

Normative profiles of neuroretinal rim area in a multiethnic Asian population: the Singapore Epidemiology of Eye Diseases study.

AIMS: To evaluate the normative profiles for neuroretinal rim area (RA) in a multiethnic Asian population. METHODS: Subjects were recruited from the Singapore Epidemiology of Eye Diseases (2009-2015) study and underwent standardised examinations. RA measurements were performed using Cirrus high-definition optical coherence tomography (Carl Zeiss Meditec). Multivariable linear regression with generalised estimating equation model was used to evaluate the associations between demographic, systemic and ocular factors with RA. RESULTS: A total of 9394 eyes from 5116 subjects (1724 Chinese, 1463 Malay, 1929 Indian) were included in the final analysis. The mean (±SD) of RA was 1.28 (±0.23) mm2 for Chinese, 1.33 (±0.26) mm2 for Malays, and 1.23 (±0.23) mm2 for Indians. The 5th percentile value for RA was 0.94 mm2 for Chinese, 0.96 mm2 for Malay, and 0.89 mm2 for Indian. In multivariable analysis, following adjustment for age, gender, body mass index, diabetes mellitus, hyperlipidaemia, history of cataract surgery, axial length, intraocular pressure (IOP) and disc area, Indian eyes have smaller RA when compared with Malays (ß=-0.074; 95% CI -0.090 to -0.058; p<0.001) and Chinese (ß=-0.035; 95% CI -0.051 to -0.019; p<0.001), respectively. Additionally, older age (per decade, ß=-0.022), male gender (ß=-0.031), longer axial length (per mm, ß=-0.025), spherical equivalent (per negative dioptre, ß=-0.005), higher IOP (per mm Hg, ß=-0.009) were associated with smaller RA (all p≤0.004). CONCLUSION: In this multiethnic population-based study, we observed significantly smaller RA in Indian eyes, compared with Chinese and Malays. This indicates the need of a more refined ethnic-specific RA normative databases among Asians.

Utilisation of poor-quality optical coherence tomography scans: adjustment algorithm from the Singapore Epidemiology of Eye Diseases (SEED) study.

PURPOSE: To evaluate the effect of signal strength (SS) on optical coherence tomography (OCT) parameters, and devise an algorithm to adjust the effect, when acceptable SS cannot be obtained. METHODS: 5085 individuals (9582 eyes), aged ≥40 years from the Singapore Epidemiology of Eye Diseases population-based study were included. Everyone underwent a standardised ocular examination and imaging with Cirrus HD-OCT. Effect of SS was evaluated using multiple structural breaks linear mixed-effect models. Expected change for increment in SS between 4 and 10 for individual parameter was calculated. Subsequently we devised and evaluated an algorithm to adjust OCT parameters to higher SS. RESULTS: Average retinal nerve fibre layer (RNFL) thickness showed shift of 4.11 µm from SS of 5 to 6. Above 6, it increased by 1.72 and 3.35 µm to 7 and 8; and by 1.09 µm (per unit increase) above 8 SS. Average ganglion cell-inner plexiform layer (GCIPL) thickness shifted 5.15 µm from SS of 5 to 6. Above 6, increased by 0.94 µm from 7 to 8; and by 0.16 µm (per unit increase) above 8 SS. When compared with reference in an independent test set, the algorithm produced less systemic bias. Algorithm-adjusted average RNFL was 0.549 µm thinner than the reference, while the unadjusted one was 2.841 µm thinner (p<0.001). Algorithm-adjusted and unadjusted average GCIPL was 1.102 µm and 2.228 µm thinner (p<0.001). CONCLUSIONS: OCT parameters can be adjusted for poor SS using an algorithm. This can potentially assist in diagnosis and monitoring of glaucoma when scans with acceptable SS cannot be acquired from patients in clinics.

Gender Prediction for a Multiethnic Population via Deep Learning Across Different Retinal Fundus Photograph Fields: Retrospective Cross-sectional Study.

BACKGROUND: Deep learning algorithms have been built for the detection of systemic and eye diseases based on fundus photographs. The retina possesses features that can be affected by gender differences, and the extent to which these features are captured via photography differs depending on the retinal image field. OBJECTIVE: We aimed to compare deep learning algorithms' performance in predicting gender based on different fields of fundus photographs (optic disc-centered, macula-centered, and peripheral fields). METHODS: This retrospective cross-sectional study included 172,170 fundus photographs of 9956 adults aged ≥40 years from the Singapore Epidemiology of Eye Diseases Study. Optic disc-centered, macula-centered, and peripheral field fundus images were included in this study as input data for a deep learning model for gender prediction. Performance was estimated at the individual level and image level. Receiver operating characteristic curves for binary classification were calculated. RESULTS: The deep learning algorithms predicted gender with an area under the receiver operating characteristic curve (AUC) of 0.94 at the individual level and an AUC of 0.87 at the image level. Across the three image field types, the best performance was seen when using optic disc-centered field images (younger subgroups: AUC=0.91; older subgroups: AUC=0.86), and algorithms that used peripheral field images had the lowest performance (younger subgroups: AUC=0.85; older subgroups: AUC=0.76). Across the three ethnic subgroups, algorithm performance was lowest in the Indian subgroup (AUC=0.88) compared to that in the Malay (AUC=0.91) and Chinese (AUC=0.91) subgroups when the algorithms were tested on optic disc-centered images. Algorithms' performance in gender prediction at the image level was better in younger subgroups (aged <65 years; AUC=0.89) than in older subgroups (aged ≥65 years; AUC=0.82). CONCLUSIONS: We confirmed that gender among the Asian population can be predicted with fundus photographs by using deep learning, and our algorithms' performance in terms of gender prediction differed according to the field of fundus photographs, age subgroups, and ethnic groups. Our work provides a further understanding of using deep learning models for the prediction of gender-related diseases. Further validation of our findings is still needed.

Deep learning algorithms to isolate and quantify the structures of the anterior segment in optical coherence tomography images.

BACKGROUND/AIMS: Accurate isolation and quantification of intraocular dimensions in the anterior segment (AS) of the eye using optical coherence tomography (OCT) images is important in the diagnosis and treatment of many eye diseases, especially angle-closure glaucoma. METHOD: In this study, we developed a deep convolutional neural network (DCNN) for the localisation of the scleral spur; moreover, we introduced an information-rich segmentation approach for this localisation problem. An ensemble of DCNNs for the segmentation of AS structures (iris, corneosclera shell adn anterior chamber) was developed. Based on the results of two previous processes, an algorithm to automatically quantify clinically important measurements were created. 200 images from 58 patients (100 eyes) were used for testing. RESULTS: With limited training data, the DCNN was able to detect the scleral spur on unseen anterior segment optical coherence tomography (ASOCT) images as accurately as an experienced ophthalmologist on the given test dataset and simultaneously isolated the AS structures with a Dice coefficient of 95.7%. We then automatically extracted eight clinically relevant ASOCT measurements and proposed an automated quality check process that asserts the reliability of these measurements. When combined with an OCT machine capable of imaging multiple radial sections, the algorithms can provide a more complete objective assessment. The total segmentation and measurement time for a single scan is less than 2 s. CONCLUSION: This is an essential step towards providing a robust automated framework for reliable quantification of ASOCT scans, for applications in the diagnosis and management of angle-closure glaucoma.

Albuminuria and Primary Open-Angle Glaucoma: the Singapore Chinese Eye Study (SCES).

AIMS: To determine the association between albuminuria and primary open-angle glaucoma (POAG). METHODS: Participants of the Singapore Chinese Eye study were recruited and underwent standardised ocular and systemic examinations. Albuminuria was determined using urinary albumin-to-creatinine ratio (UACR, mg/g) based on random spot urinary albumin and creatinine measurements. POAG was defined using the International Society of Geographic and Epidemiological Ophthalmology classification. Multivariable logistic regression with generalised estimating equation model was used to evaluate the association between albuminuria and POAG, while accounting for correlation between eyes. RESULTS: A total of 3009 Chinese adults (5963 eyes), aged 40-80 years, were included in this study, of which, 52 subjects (75 eyes) had POAG. Higher UACR (per 50 mg/g increase) was independently associated with POAG (OR=1.04, 95% CI 1.01 to 1.07, p=0.003) following adjustment for age, gender, intraocular pressure, diabetes mellitus, hyperlipidaemia, hypertension, anti-hypertensive medication, history of cardiovascular disease, current smoking status, alcohol intake, body mass index and estimated glomerular filtration rate. Further stratification revealed that individuals with macroalbuminuria were 8.00 times likely to have POAG (95% CI 2.97 to 21.54, p<0.001), compared with those with normoalbuminuria. Microalbuminuria was not significantly associated with POAG (OR=0.49, 95% CI 0.19 to 1.29, p=0.150). The association between macroalbuminuria and POAG remained significant among individuals who were diabetic (OR=9.89, 95% CI 2.49 to 39.30, p=0.001) and hypertensive (OR=8.39, 95% CI 3.07 to 22.94, p<0.001). CONCLUSION: In this population-based study of Chinese adults, albuminuria was independently associated with POAG. Our findings provide further understanding on the pathogenesis of POAG and may potentially help to better identify individuals at risk of POAG.

Comparison of myopic progression in Finnish and Singaporean children.

PURPOSE: To compare 3-year myopic progression between Finnish and Singaporean children. METHODS: Myopic progression was compared between 9-year-old (mean age 9.7 ± 0.4 years, n = 92) and 11-year-old (mean age 11.7 ± 0.4 years, n = 144) Finnish (Finnish RCT) children and Singaporean children matched by age and refraction (SCORMMatched, n = 403) and 7- to 8-year-old Singaporean children matched only by refraction (SCORM Young, n = 186). Spherical equivalent (SE) was between -0.50 and -3.00 D. Refraction with cycloplegia was controlled annually for 3 years. Information on parental myopia, mother's education, time spent on near-work and outdoor time was gathered by parental questionnaire. RESULTS: Three-year myopic progression was -2.08 ± 0.96 D and -1.30 ± 0.69 D in the Finnish RCT and Singaporean SCORM Matched 9-year-olds, respectively, and -1.34 ± 0.78 D, and -0.52 ± 0.44 D in the 11-year-olds, respectively (p < 0.001 between all groups). Myopic progression was fastest (-2.69 ± 0.89 D) in the SCORM 7-year-olds and similar between the SCORM Matched 9-year-olds and Finnish RCT 11-year-olds (p = 0.55). The Finnish RCT and SCORM Matched children showed significant differences in both daily near-work time (1.8 ± 1.0 versus 3.4 ± 1.9 hours per day, p < 0.001) and outdoor time (2.6 ± 0.9 versus 0.5 ± 0.4 hours per day, p < 0.001). These differences did not, however, explain the differences in myopic progression between the groups. More time spent outdoors was associated with less myopic progression in the Finnish RCT (r = 0.17, p = 0.009) group only. In the whole materials, greater myopic progression was associated with younger age at baseline (p < 0.001), younger age was associated with mother's higher education (p < 0.001), and mothers higher education was associated with myopia in both parents (p < 0.001). CONCLUSION: Age at baseline was the most significant factor associated with myopic progression. However, at the same age and with the same initial refraction, the Finnish and Singaporean children showed different myopic progression. This result remains unexplained. Thus, age of myopia onset should be considered when comparing myopic progression between different samples and conducting treatment trials. Parental myopia may be a weak indicator of heredity of myopia.