Predictive Potential of Retina-Based Biological Age in Assessing Chronic Obstructive Pulmonary Disease Risk.

Background: Previously, based on retinal photographs, we developed a deep-learning algorithm to predict biological age (termed, RetiAGE) that was associated with future risks of morbidity and mortality. This study specifically aimed to evaluate the performance of RetiAGE in predicting future risks of chronic obstructive pulmonary disease (COPD).

Methods: RetiAGE scores were generated from retinal images in the UK Biobank and stratified into tertiles.

Physiological change in ganglion cell inner plexiform layer and nerve fibre layer thickness over six years.

Background: To evaluate the 6-year physiological rates-of-change in ganglion cell inner plexiform layer (GCIPL) and retinal nerve fibre layer (RNFL) thickness measured with optical coherence tomography.

Methods: We included 2202 out of 2661 subjects from the population-based Singapore Chinese Eye Study who returned for follow-up 6 years after baseline examination (follow-up rate 87.7%).

Omega-3 Polyunsaturated Fatty Acids as a Protective Factor for Myopia.

Purpose: Animal models suggest omega-3 polyunsaturated fatty acids (PUFAs) may protect against myopia by modulating choroidal blood perfusion, but clinical evidence is scarce and mixed. We aimed to determine the causality between omega-3 PUFAs and myopia using Mendelian randomization (MR) analysis.

Design: Two-sample MR analysis.

Prognostic potentials of AI in ophthalmology: systemic disease forecasting via retinal imaging.

Background: Artificial intelligence (AI) that utilizes deep learning (DL) has potential for systemic disease prediction using retinal imaging. The retina's unique features enable non-invasive visualization of the central nervous system and microvascular circulation, aiding early detection and personalized treatment plans for personalized care. This review explores the value of retinal assessment, AI-based retinal biomarkers, and the importance of longitudinal prediction models in personalized care.

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.

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).

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.

Associations between Chronic Kidney Disease and Thinning of Neuroretinal Layers in Multiethnic Asian and White Populations.

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.

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.

Six-Year Incidence and Risk Factors for Primary Open-Angle Glaucoma and Ocular Hypertension: The Singapore Epidemiology of Eye Diseases Study.

Objective: To determine the incidence and risk factors for primary open-angle glaucoma (POAG) and ocular hypertension (OHT) in a multiethnic Asian population.

Design: Population-based cohort study.

Participants: The Singapore Epidemiology of Eye Diseases study included 10 033 participants in the baseline examination between 2004 and 2011.

Application of big data in ophthalmology.

The advents of information technologies have led to the creation of ever-larger datasets. Also known as , 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.

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).

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.

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.

Retinal photograph-based deep learning predicts biological age, and stratifies morbidity and mortality risk.

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.

Multivariate Normative Comparison, a Novel Method for Improved Use of Retinal Nerve Fiber Layer Thickness to Detect Early Glaucoma.

Purpose: Detection of early glaucoma remains limited with the conventional analysis of the retinal nerve fiber layer (RNFL). This study assessed whether compensating the RNFL thickness for multiple demographic and anatomic factors improves the detection of glaucoma.

Design: Cross-sectional study.

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).