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Background ageing is an important risk factor for a variety of human pathologies. Biological age (BA) may better capture ageing-related physiological changes compared with chronological age (CA). Objective we developed a deep learning (DL) algorithm to predict BA based on retinal photographs and evaluated the performance of our new ageing marker in the risk stratification of mortality and major morbidity in general populations. Methods we first trained a DL algorithm using 129,236 retinal photographs from 40,480 participants in the Korean Health Screening study to predict the probability of age being ≥65 years ('RetiAGE') and then evaluated the ability of RetiAGE to stratify the risk of mortality and major morbidity among 56,301 participants in the UK Biobank. Cox proportional hazards model was used to estimate the hazard ratios (HRs). Results in the UK Biobank, over a 10-year follow up, 2,236 (4.0%) died; of them, 636 (28.4%) were due to cardiovascular diseases (CVDs) and 1,276 (57.1%) due to cancers. Compared with the participants in the RetiAGE first quartile, those in the RetiAGE fourth quartile had a 67% higher risk of 10-year all-cause mortality (HR = 1.67 [1.42–1.95]), a 142% higher risk of CVD mortality (HR = 2.42 [1.69–3.48]) and a 60% higher risk of cancer mortality (HR = 1.60 [1.31–1.96]), independent of CA and established ageing phenotypic biomarkers. Likewise, compared with the first quartile group, the risk of CVD and cancer events in the fourth quartile group increased by 39% (HR = 1.39 [1.14–1.69]) and 18% (HR = 1.18 [1.10–1.26]), respectively. The best discrimination ability for RetiAGE alone was found for CVD mortality (c-index = 0.70, sensitivity = 0.76, specificity = 0.55). Furthermore, adding RetiAGE increased the discrimination ability of the model beyond CA and phenotypic biomarkers (increment in c-index between 1 and 2%). Conclusions the DL-derived RetiAGE provides a novel, alternative approach to measure ageing. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. No commercial re-use. See rights and permissions. Published by BMJ.)

Objective: To develop and validate a deep learning algorithm that could automate the annotation of scleral spur (SS) and segmentation of anterior chamber (AC) structures for measurements of AC, iris, and angle width parameters in anterior segment OCT (ASOCT) scans., Design: Cross-sectional study., Subjects: Data from 2 population-based studies (i.e., the Singapore Chinese Eye Study and Singapore Malay Eye Study) and 1 clinical study on angle-closure disease were included in algorithm development. A separate clinical study on angle-closure disease was used for external validation., Method: Image contrast of ASOCT scans were first enhanced with CycleGAN. We utilized a heat map regression approach with coarse-to-fine framework for SS annotation. Then, an ensemble network of U-Net, full resolution residual network, and full resolution U-Net was used for structure segmentation. Measurements obtained from predicted SSs and structure segmentation were measured and compared with measurements obtained from manual SS annotation and structure segmentation (i.e., ground truth)., Main Outcome Measures: We measured Euclidean distance and intraclass correlation coefficients (ICC) to evaluate SS annotation and Dice similarity coefficient for structure segmentation. The ICC, Bland-Altman plot, and repeatability coefficient were used to evaluate agreement and precision of measurements., Results: For SS annotation, our algorithm achieved a Euclidean distance of 124.7 μm, ICC ≥ 0.95, and a 3.3% error rate. For structure segmentation, we obtained Dice similarity coefficient ≥ 0.91 for cornea, iris, and AC segmentation. For angle width measurements, ≥ 95% of data points were within the 95% limits-of-agreement in Bland-Altman plot with insignificant systematic bias (all P > 0.12). The ICC ranged from 0.71-0.87 for angle width measurements, 0.54 for IT750, 0.83-0.85 for other iris measurements, and 0.89-0.99 for AC measurements. Using the same SS coordinates from a human expert, measurements obtained from our algorithm were generally less variable than measurements obtained from a semiautomated angle assessment program., Conclusion: We developed a deep learning algorithm that could automate SS annotation and structure segmentation in ASOCT scans like human experts, in both open-angle and angle-closure eyes. This algorithm reduces the time needed and subjectivity in obtaining ASOCT measurements., Financial Disclosures: The author(s) have no proprietary or commercial interest in any materials discussed in this article., (© 2023 Published by Elsevier Inc. on behalf of the American Academy of Ophthalmology.)

Purpose: To evaluate the relationships between chronic kidney disease (CKD) with retinal nerve fiber layer (RNFL) and ganglion cell-inner plexiform layer (GCIPL) thickness profiles of eyes in Asian and White populations., Design: Cross-sectional analysis., Participants: A total of 5066 Asian participants (1367 Malays, 1772 Indians, and 1927 Chinese) from the Singapore Epidemiology of Eye Diseases Study (SEED) were included, consisting of 9594 eyes for peripapillary RNFL analysis and 8661 eyes for GCIPL analysis. Additionally, 45 064 White participants (87 649 eyes) from the United Kingdom Biobank (UKBB) were included for both macular RNFL analysis and GCIPL analysis., Methods: Nonglaucoma participants aged ≥ 40 years with complete data for estimated glomerular filtration rate (eGFR) were included from both SEED and UKBB. In SEED, peripapillary RNFL and GCIPL thickness were measured by Cirrus HD-OCT 4000. In UKBB, macular RNFL and GCIPL were measured by Topcon 3D-OCT 1000 Mark II. Chronic kidney disease was defined as eGFR < 60 ml/min/1.73 m 2 in both data sets. To evaluate the associations between kidney function status with RNFL and GCIPL thickness profiles, multivariable linear regression with generalized estimating equation models were performed in SEED and UKBB data sets separately., Main Outcome Measures: Average peripapillary and macular RNFL thickness and macular GCIPL thickness., Results: In SEED, after adjusting for age, gender, ethnicity, systolic blood pressure, antihypertensive medication, diabetes, hyperlipidemia, body mass index, smoking status, and intraocular pressure, presence of CKD (β = -1.31; 95% confidence interval [CI], -2.37 to -0.26; P  = 0.015) and reduced eGFR (per 10 ml/min/1.73 m 2 ; β = -0.32; 95% CI, -0.50 to -0.13; P  = 0.001) were associated with thinner average peripapillary RNFL. Presence of CKD (β = -1.63; 95% CI, -2.42 to -0.84) and reduced eGFR (per 10 ml/min/1.73 m 2 ; β = -0.30; 95% CI, -0.44 to -0.16) were consistently associated with thinner GCIPL in SEED (all P < 0.001). In UKBB, after adjusting for the above-mentioned covariates (except ethnicity), reduced eGFR (per 10 ml/min/1.73 m 2 ; β = -0.06; 95% CI, -0.10 to -0.01; P  = 0.008) was associated with thinner macular RNFL and CKD (β = -0.62; 95% CI, -1.16 to -0.08; P  = 0.024) was associated with thinner average GCIPL., Conclusion: We consistently observed associations between CKD and thinning of RNFL and GCIPL across Asian and White populations' eyes. These findings further suggest that compromised kidney function is associated with RNFL and GCIPL thinning., Financial Disclosures: The author(s) have no proprietary or commercial interest in any materials discussed in this article., (© 2023 by the American Academy of 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., Competing Interests: The authors declare that there are no conflicts of interest in this paper., (Copyright: © 2023 Taiwan J Ophthalmol.)