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Ocular Epidemiology Research Group


The Ocular Epidemiology Research Group, led by Prof Cheng Ching-Yu, aims to develop and facilitate collaborative and translational research by integrating big data, omics, and machine learning analytics into large scale population-based studies. The strategic goal of our group is to improve the eye health of populations in Singapore and globally, and to become one of the leading international centres for population science and digital health of eye diseases by:

  • using state-of-art data analytics and artificial intelligence techniques,

  • establishing collaborative research platform, and

  • nurturing the next generation of researchers and scientists. 

Our Vision

  • Build a world-leading research programme focusing specifically on the epidemiology and population health of major eye diseases in Asia.

  • Provide a one-stop “data portal” and information source on the population health of Asian eye diseases.

  • Foster international collaborations with other population health and ophthalmic institutes in Asia and worldwide.

Our Aims

  • To document the prevalence, incidence, risk factors and public health significance of blinding eye diseases in Singapore and Asia by conducting large scale epidemiological studies under the umbrella of ‘Singapore Epidemiology of Eye Diseases (SEED)’ Programme.

  • To support research initiatives by developing and maintaining population data, biospecimen, genomic resources, images, and analytics methodologies.

  • To bridge the gap between population health and clinical applications by leveraging existing biospecimen resources in discovery or validation of biomarkers for risk stratification, and developing novel screening modalities based on deep learning technologies.

  • To provide research expertise, training and consultation to other researchers and ophthalmic institutions in Singapore, the Asia-Pacific region and globally.

Research Competencies and Expertise

The Ocular Epidemiology Research Group brings together innovative population health research and cutting-edge technology, such as a next-generation sequencing and artificial intelligence, with a focus on our theme-oriented strategy. Our group has multi-disciplinary expertise in all aspects of clinical and epidemiological research. We mainly research and develop the following areas:

  • Translational Population Health

  • Big Data Analytics and Artificial Intelligence

  • Omics and Genetic Epidemiology

  • Infrastructure of Population Science


Population Cohorts


  Age range



  Data Collected

Singapore Malay Eye Study (SiMES)

40-80 years



Prevalence, risk factors, and impact of visual impairment and major eye diseases in Singaporean Malays

Singapore Indian Eye Study (SINDI)

40-80 years



​Prevalence, risk factors, and impact of visual impairment and major eye diseases in Singaporean Indians

Singapore Chinese Eye Study (SCES)

40-80 years



​Prevalence, risk factors, and impact of visual impairment and major eye diseases in Singaporean Chinese

The Singapore Prospective Study Programme (SP2) Ancillary study

24-95 years

Chinese, Malays and Indians


Prevalence, environmental and genetic risk factors, and impacts of cardiovascular and metabolic diseases (e.g., hypertension, dyslipidemia, obesity, and diabetes mellitus)

Polyclinic Study

50+ years



Ocular and image biomarkers for glaucoma


Cohort Highlights

  • Singapore Singapore Epidemiology of Eye Diseases (SEED) Study

The SEED study is a multi-ethnic longitudinal population-based study, comprised of over 10,000 adult participants aged 40 years or older in Singapore. It included three large population-based studies: the Singapore Malay Eye Study (SiMES), the Singapore Indian Eye Study (SINDI), and the Singapore Chinese Eye Study (SCES), with a focus on studying major eye diseases, including diabetic retinopathy, age-related macular degeneration, glaucoma, refractive errors and cataract. As one of the largest epidemiological databases and biobanks for eye diseases globally, SEED data has been widely used by national and international agencies to help guide public policy decisions on screening and early detection of age-related eye diseases. Furthermore, SEED findings have also contributed to other epidemiological areas such as machine/deep learning and genetic discoveries. More detailed information regarding SEED study can be found here.

  • Asian Epidemiology of Eye Diseases (AEEC) Consortium

The Asian Eye Epidemiology Consortium (AEEC) is a collaborative network of population-based studies performed across Asia with the overall aim of developing large datasets to provide deeper insights on the trends and associated risk factors of major age-related eye diseases among Asians. The AEEC network consist of over 40 population-based studies originating from at least 10 different Asian countries. Currently, data from the AEEC network has provided key insights into major age-related eye diseases in Asians, such as geographic atrophy, primary open-angle glaucoma,  myopia, and diabetic retinopathy. We welcome new population studies to join the consortium.

Current Research Grants and Projects


  Principal Investigator


​Characterising the Role of Iris Surface Features in Iris Sponginess and their Relevance to Angle-closure Glaucoma​Prof Cheng Ching-Yu​2016 - 2021
​Deep Phenotyping and Genetic Landscaping of Primary Open Angle Glaucoma​Prof Cheng Ching-Yu​2017 - 2022
​Diabetes Study on Nephropathy and Other Microvascular Complications (DYNAMO): Retinal Microvasculature as a Window to Study Mechanisms and Pathways in Diabetic NephropathyProf Wong Tien Yin​2017 - 2022
​The Singapore Epidemiology of Eye Diseases (SEED) Study 3: Prospective Multi-ethnic Cohort Study of 12-year Incidence, Risk factors, and Impact of Major Age-related Eye Diseases​Prof Cheng Ching-Yu​2018 - 2022
​Translational Asian Age-related Macular Degeneration Programme (TAAP): Population Health: Characterising Disease Burden, Novel and Genetic Risk Factors in Asians​Prof Cheng Ching-Yu & Prof Wong Tien Yin​2018 - 2023
​Artificial Intelligence Programme in Diabetic Retinopathy and Complications​Prof Wong Tien Yin​2019 - 2021

Artificial Intelligence for Functional VIsion Screening Using Retinal Imaging (AVIRI)

Dr Tham Yih Chung

2019 - 2021

Community-based Screening for Pathological Visual Impairment among Elderly Residents using Artificial-Intelligence Integrated Retinal Imaging

Dr Tham Yih Chung

2019 - 2022

​COVID-19 Pandemic: Triaging of ‘Only Urgent Eye Referrals’ from Polyclinics (TOP) using Retinal Photograph-based Deep Learning​Dr Tham Yih Chung​2020 - 2021

From Machine to Machine-developing a Deep Learning Algorithm for Quantification of Ocular Traits based on Retinal Photographs

Dr Tyler Hyungtaek Rim

2020 - 2021

Implementation of Community-based Elderly Health Care for Eye and Systemic Diseases Using Automated Screening

Dr Tyler Hyungtaek Rim

2020 - 2022

SERI-based Machine Learning and AI Talent (SMAT) Programme

Dr Tyler Hyungtaek Rim

2019 - 2021

The Retina as a Window to Vascular and Neurological Disorders

Prof Wong Tien Yin

2021 - 2022

Digital and Precision Community Screening Platform for Ageing Diseases: Vision, Metabolism and Heart

Prof Cheng Ching-Yu

2021 - 2025


Project Highlights

  • New AI-assisted Vision Screening Model for Community

Visual impairment is a major public health problem, associated with reduced quality of life and increased risk of frailty and mortality. Globally in 2020, an estimated 553 million people had visual impairment and 43 million were blind. 40% of visual impairment is related to refractive error that requires the provision of spectacles in community settings; however, the remaining 60% of cases cannot be corrected with spectacles and require assessment, diagnosis, treatment, and possibly surgery in eye-care settings led by ophthalmologists. These 60% of people with visual impairment can be referred to as having disease-related visual impairment (i.e., substantial loss of vision caused by an eye disease, and unrelated to refractive error). If detected early, these conditions can be treated, thus preventing or slowing development of vision loss. We developed a single-modality, retinal photograph-based deep learning algorithm (termed AVIRI) to detect disease-related visual impairment, using a total of 15,175 eyes from a multi-ethnic Asian population-based eye study. We also did independent validation of the algorithm using datasets of eyes from three other population-based studies and two clinic-based studies (total of 16,963 eyes), which generally showed that the algorithm had optimal performance. This is the first study to show the use of a single-modality deep learning algorithm, using only a single macular-centred retinal photograph, for identification. The unique design of this algorithm enables it to potentially be used as an efficient automated referral tool in community screening. Moving forward, there are plans to perform real-world validation to enable translation of this innovation for community screening use. Our work was published in Lancet Digital Health and was featured as Editor’s pick. More details can be found here.

  • Deep-learning-based Cardiovascular Risk Stratification using Coronary Artery Calcium Scores Predicted from Retinal Photographs

Cardiovascular disease is the leading cause of death worldwide. The retina is the only organ that allows direct, non-invasive, in-vivo visualisation of the microvasculature and neural tissues. In recent decades, our understanding of retina-systemic relationships has relied on classic epidemiological studies based on observable, human-defined retinal features (e.g., retinopathy or retinal vascular calibre). The potential discovery of unobservable retinal features associated with systemic diseases has been enhanced by advances in artificial intelligence technology, specifically deep learning. Coronary artery calcium (CAC) is a preclinical marker of atherosclerosis and is strongly associated with risk of clinical cardiovascular disease. We have extended this concept of retina-systemic relationships and hypothesise that retinal photograph-based deep learning can also predict CAC score, and this retinal-predicted score (termed “RetiCAC) can also be used as a risk stratification tool for cardiovascular events. In our study, similar to the current CT-measured CAC stratification system, the relative risk of cardiovascular disease events showed a dose-response association across the three risk strata. Overall, the proposed new stratification system based on RetiCAC score showed comparable performance in predicting cardiovascular disease events compared with conventional CT-measured CAC score. Thus, retinal photography could potentially be adopted as a relatively simple and non-radiation imaging modality for cardiovascular disease risk classification. Our work was published in Lancet Digital Health.

  • A Deep Learning Algorithm to Detect Chronic Kidney Disease from Retinal Photographs in Community-based Populations

Chronic kidney disease (CKD) is a major health condition associated with significant morbidity, cardiovascular disease and mortality. Screening for CKD is challenging in community and primary care settings, even in high-income countries because of the need to obtain serum levels of creatinine, or testing urine for protein. The retina being accessible to non-invasive imaging and retinal changes have been shown to provide information on systemic vascular and metabolic diseases, we developed an artificial intelligence deep learning algorithm (DLA) to detect CKD from retinal images, which may add to existing CKD screening strategies. We developed and validated the DLA utilising retinal images and data from the Singapore Epidemiology of Eye Diseases (SEED) and externally validated the DLA in two independent datasets in Singapore (SP2) and China (BES). We developed an ‘Image-only’ model based on macula-centred images from both eyes. For comparison, we also developed a ‘Risk-factor’ model based on key risk factors including age, sex, ethnicity, diabetes and hypertension. The image-only DLA showed an AUC of 0.911 in internal validation and 0.733 and 0.835 in external tests sets. Corresponding estimates for the risk-factor model were 0.916, 0.829 and 0.887. The image-only DLA and risk factor DLAs achieve high AUCs in SEED internal validation and modest to good AUCs in external test sets. Our findings show that for CKD detection, a retinal-image only DLA is similar to information from a classic risk-factor model and support the potential of a retinal-imaged based DLA to be adopted for first stage CKD screening before confirmation by serum creatinine. Our work was published in Lancet Digital Health. More details can be found here.

Achievements and Impact

We have established Singapore as a leading hub of ophthalmic epidemiology, clinical and genetic research in Asia with a particular focus on diseases that are prevalent in this region. Data collected from our group has been used widely by national and international agencies (e.g. the Ministry of Health (MOH) Singapore, the World Health Organization, the Global Burden of Disease programme, etc.) and clinical guidelines (e.g. 2014 MOH Diabetes Guidelines, 2016 Asia Pacific Glaucoma Guidelines, 2016 American Diabetes Association Guidelines, 2017 International Council of Ophthalmology Diabetic Eye Care Guidelines). The data has been used to provide estimates of eye disease burden to set up the national DR screening. It also assists both the MOH’s planning for healthcare manpower (future ophthalmology and optometry manpower) and the College of Ophthalmology’s planning for the National Ophthalmology Road Map 2030 plan in Singapore.

In addition, we also provide research expertise, resource, and consultation for other research centres, hospitals and ophthalmic institutions in Singapore and Asia. We conduct training programmes for clinicians, research fellows and graduate students interested in big data analytics, digital health, and ophthalmic epidemiology.


The list below is selected from more than 600 publications.

  1. Cheng CY*, Schache M*, Ikram MK*, Young TL*, Guggenheim JA*, Vitart V*, MacGregor S*, Verhoeven VJM, Barathi VA, Liao J, Hysi PG, Bailey-Wilson JE, St. Pourcain B, Kemp JP, McMahon G, Timpson NJ, Evans DM, Montgomery GW, Mishra A, Wang YX, Wang JJ, Rochtchina E, Polasek O, Wright AF, Amin N, Van Leeuwen EM, Wilson JF, Pennell CE, Van Duijn CM, De Jong PTVM, Vingerling JR, Zhou X, Chen P, Li R, Tay WT, Zheng YF, Chew M, Burdon KP, Craig JE, Iyengar SK, Igo Jr. RP, Lass Jr. JH, Chew EY, Haller T, Mihailov E, Metspalu A, Wedenoja J, Simpson CL, Wojciechowski R, Höhn R, Mirshahi A, Zeller T, Pfeiffer N, Lackner KJ, Bettecken T, Meitinger T, Oexle K, Pirastu M, Portas L, Nag A, Williams KM, Yonova-Doing E, Klein R, Klein BE, Hosseini SM, Paterson AD, Makela K-, Lehtimaki T, Kahonen M, Raitakari O, Yoshimura N, Matsuda F, Chen LJ, Pang CP, Yip SP, Yap MKH, Meguro A, Mizuki N, Inoko H, Foster PJ, Zhao JH, Vithana E, Tai E-, Fan Q, Xu L, Campbell H, Fleck B, Rudan I, Aung T, Hofman A, Uitterlinden AG, Bencic G, Khor C-, Forward H, Pärssinen O, Mitchell P, Rivadeneira F, Hewitt AW, Williams C, Oostra BA, Teo Y-, Hammond CJ, Stambolian D*, MacKey DA*, Klaver CCW*, Wong TY*, Saw SM*, Baird PN*. Nine loci for ocular axial length identified through genome-wide association studies, including shared loci with refractive error. Am J Hum Genet. 2013 Aug; 93(2):264-77.
  2. Wong WL, Su X, Li X, Cheung CM, Klein R, Cheng CY, Wong TY. Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: A systematic review and meta-analysis. Lancet Glob Health. 2014 Feb;2(2):e106 - e116.
  3. Sidhartha E, Gupta P, Liao J, Tham YC, Cheung CY, He M, Wong TY, Aung T, Cheng CY. Assessment of iris surface features and their relationship with iris thickness in Asian eyes. Ophthalmology. 2014 May;121(5):1007-12.
  4. Wong CW, Wong TY, Cheng CY, Sabanayagam C. Kidney and eye diseases: Common risk factors, etiological mechanisms, and pathways. Kidney Int. 2014 Jun;85(6):1290-302.
  5. Tham YC, Li X, Wong TY, Quigley HA, Aung T, Cheng CY. Global prevalence of glaucoma and projections of glaucoma burden through 2040: A systematic review and meta-analysis. Ophthalmology. 2014 Nov;121(11):2081-90.
  6. Hysi PG*, Cheng CY*, Springelkamp H*, Macgregor S*, Bailey JN*, Wojciechowski R*, Vitart V, Nag A, Hewitt AW, Höhn R, Venturini C, Mirshahi A, Ramdas WD, Thorleifsson G, Vithana E, Khor CC, Stefansson AB, Liao J, Haines JL, Amin N, Wang YX, Wild PS, Ozel AB, Li JZ, Fleck BW, Zeller T, Staffieri SE, Teo YY, Cuellar-Partida G, Luo X, Allingham RR, Richards JE, Senft A, Karssen LC, Zheng Y, Bellenguez C, Xu L, Iglesias AI, Wilson JF, Kang JH, van Leeuwen EM, Jonsson V, Thorsteinsdottir U, Despriet DD, Ennis S, Moroi SE, Martin NG, Jansonius NM, Yazar S, Tai ES, Amouyel P, Kirwan J, van Koolwijk LM, Hauser MA, Jonasson F, Leo P, Loomis SJ, Fogarty R, Rivadeneira F, Kearns L, Lackner KJ, de Jong PT, Simpson CL, Pennell CE, Oostra BA, Uitterlinden AG, Saw SM, Lotery AJ, Bailey-Wilson JE, Hofman A, Vingerling JR, Maubaret C, Pfeiffer N, Wolfs RC, Lemij HG, Young TL, Pasquale LR, Delcourt C, Spector TD, Klaver CC, Small KS, Burdon KP, Stefansson K, Wong TY; BMES GWAS Group; NEIGHBORHOOD Consortium; Wellcome Trust Case Control Consortium 2, Viswanathan A*, Mackey DA*, Craig JE*, Wiggs JL*, van Duijn CM*, Hammond CJ*, Aung T*. Genome-wide analysis of multi-ancestry cohorts identifies new loci influencing intraocular pressure and susceptibility to glaucoma. Nat Genet. 2014 Oct;46(10):1126-30.
  7. Cheng CY*, Yamashiro K*, Jia Chen L*, Ahn J*, Huang L*, Huang L*, Cheung CM, Miyake M, Cackett PD, Yeo IY, Laude A, Mathur R, Pang J, Sim KS, Koh AH, Chen P, Lee SY, Wong D, Chan CM, Loh BK, Sun Y, Davila S, Nakata I, Nakanishi H, Akagi-Kurashige Y, Gotoh N, Tsujikawa A, Matsuda F, Mori K, Yoneya S, Sakurada Y, Iijima H, Iida T, Honda S, Lai TY, Tam PO, Chen H, Tang S, Ding X, Wen F, Lu F, Zhang X, Shi Y, Zhao P, Zhao B, Sang J, Gong B, Dorajoo R, Yuan JM, Koh WP, van Dam RM, Friedlander Y, Lin Y, Hibberd ML, Foo JN, Wang N, Wong CH, Tan GS, Park SJ, Bhargava M, Gopal L, Naing T, Liao J, Guan Ong P, Mitchell P, Zhou P, Xie X, Liang J, Mei J, Jin X, Saw SM, Ozaki M, Mizoguchi T, Kurimoto Y, Woo SJ, Chung H, Yu HG, Shin JY, Park DH, Kim IT, Chang W, Sagong M, Lee SJ, Kim HW, Lee JE, Li Y, Liu J, Teo YY, Heng CK, Lim TH, Yang SK, Song K, Vithana EN, Aung T, Bei JX, Zeng YX, Tai ES*, Li XX*, Yang Z*, Park KH*, Pang CP*, Yoshimura N*, Wong TY, Khor CC. New loci and coding variants confer risk for age-related macular degeneration in East Asians. Nat Commun. 2015 Jan 28;6:6063.
  8. Sabanayagam C*, Khoo EY*, Lye WK, Ikram MK, Lamoureux EL, Cheng CY, Tan ML, Salim A, Lee J, Lim SC, Tavintharan S, Thai AC, Heng D, Ma S, Tai ES*, Wong TY*. Diagnosis of diabetes mellitus using HbA1c in Asians: Relationship between HbA1c and retinopathy in a multiethnic Asian population. J Clin Endocrinol Metab. 2015 Feb;100(2):689-96.
  9. Tham YC, Liao J, Vithana EN, Khor CC, Teo YY, Tai ES, Wong TY, Aung T, Cheng CY. International Glaucoma Genetics Consortium. Aggregate effects of intraocular pressure and cup-to-disc ratio genetic variants on glaucoma in a multiethnic Asian population. Ophthalmology. 2015 Jun;122(6):1149-57.
  10. Bailey JN, Loomis SJ, Kang JH, Allingham RR, Gharahkhani P, Khor CC, …, Cheng CY, et al. Genome-wide association analysis identifies TXNRD2, ATXN2 and FOXC1 as susceptibility loci for primary open-angle glaucoma. Nat Genet. 2016 Feb;48(2):189-194.
  11. Fan Q, Verhoeven VJ, Wojciechowski R, Barathi VA, Hysi PG, Guggenheim JA, …, Cheng CY#, Hammond CJ#, Klaver CC#, Saw SM#. Meta-analysis of gene-environment-wide association scans accounting for education level identifies additional loci for refractive error. Nat Commun. 2016 Mar;7:11008. [#Co-last author]
  12. Cheng CY, Wong TY. Observations from a population-based study of diabetic retinopathy in Chinese Americans? JAMA Ophthalmol. 2016 May;134(5):569.
  13. Fuchsberger C, Flannick J, Teslovich TM, Mahajan A, Agarwala V, Gaulton KJ, …, Cheng CY, et al. The genetic architecture of type 2 diabetes. Nature. 2016 Aug; 536(7614):41-47.
  14. Sabanayagam C, Fenwick E, Ong PG, Tey ML, Tapp R, Cheng CY, Cheung GC, Aung T, Wong TY, Lamoureux E. Visual impairment in old and very old community-dwelling Asian adults. Ophthalmology. 2016 Nov;123(11):2436-2438.
  15. Chong YH, Fan Q, Tham YC, Gan A, Tan SP, Tan G, Wang JJ, Mitchell P, Wong TY, Cheng CY. Type 2 diabetes genetic variants and risk of diabetic retinopathy. Ophthalmology. 2017 Mar;124(3):336-342.
  16. Ting DSW, Cheung CY, Lim G, Tan GSW, Quang ND, Gan A, Hamzah H, Garcia-Franco R, San Yeo IY, Lee SY, Wong EYM, Sabanayagam C, Baskaran M, Ibrahim F, Tan NC, Finkelstein EA, Lamoureux EL, Wong IY, Bressler NM, Sivaprasad S, Varma R, Jonas JB, He MG, Cheng CY, Cheung GCM, Aung T, Hsu W, Lee ML, Wong TY. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA. 2017 Dec; 318(22):2211-2223.
  17. Fan Q, Maranville JC, Fritsche L, Sim X, Cheung CMG, Chen LJ, …, Cheng CY. HDL-cholesterol levels and risk of age-related macular degeneration: a multi-ethnic genetic study using Mendelian randomization. Int J Epidemiol. 2017 Dec;46(6):1891-1902.
  18. Sobrin L, Chong YH, Fan Q, Gan A, Stanwyck LK, Kaidonis G, Craig JE, Kim J, Liao WL, Huang YC, Lee WJ, Hung YJ, Guo X, Hai Y, Ipp E, Pollack S, Hancock H, Price A, Penman A, Mitchell P, Liew G, Smith AV, Gudnason V, Tan G, Klein BEK, Kuo J, Li X, Christiansen MW, Psaty BM, Sandow K; Asian Genetic Epidemiology Network Consortium, Jensen RA, Klein R, Cotch MF, Wang JJ, Jia Y, Chen CJ, Chen YI, Rotter JI, Tsai FJ, Hanis CL, Burdon KP, Wong TY, Cheng CY. Genetically determined plasma lipid levels and risk of diabetic retinopathy: A mendelian randomization study. Diabetes. 2017 Dec;66(12):3130-3141.
  19. Sabanayagam C, Cheng CY. Global causes of vision loss in 2015: Are we on track to achieve the Vision 2020 target? Lancet Glob Health. 2017 Dec;5(12):e1164-e1165.
  20. Tan NYQ, Tham YC, Koh V, Nguyen DQ, Cheung CY, Aung T, Wong TY, Cheng CY. The effect of testing reliability on visual field sensitivity in normal eyes: The Singapore Chinese Eye Study. Ophthalmology. 2018 Jan;125(1):15-21.
  21. Khawaja AP, Cooke Bailey JN, Wareham NJ, …, Cheng CY, et al. Genome-wide analyses identify 68 new loci associated with intraocular pressure and improve risk prediction for primary open-angle glaucoma. Nat Genet. 2018 Jun;50(6):778-782.
  22. Sabanayagam C, Banu R, Chee ML, Lee R, Wang YX, Tan G, Jonas JB, Lamoureux EL, Cheng CY, Klein BEK, Mitchell P, Klein R, Cheung CMG, Wong TY. Incidence and progression of diabetic retinopathy: A systematic review. Lancet Diabetes Endocrinol. 2019 Feb;7(2):140-149.
  23. Ho H, Tham YC, Chee ML, Shi Y, Tan NYQ, Wong KH, Majithia S, Cheung CY, Aung T, Wong TY, Cheng CY. Retinal nerve fiber layer thickness in a multiethnic normal Asian population: The Singapore Epidemiology of Eye Diseases (SEED) study. Ophthalmology. 2019 May;126(5):702-711.
  24. Wu D*, Dou J*, Chai X*, Bellis C, Wilm A, Shih CC, Soon WWJ, Bertin N, Lin CB, Khor CC, DeGiorgio M, Cheng S, Bao L, Karnani N, Hwang WYK, Davila S, Tan P, Shabbir A, Moh A, Tan EK, Foo JN, Goh LL, Leong KP, Foo RSY, Lam CSP, Richards AM, Cheng CY, Aung T, Wong TY, Ng HH; SG10K Consortium, Liu J*, Wang C*. Large-scale whole-genome sequencing of three diverse Asian populations in Singapore. Cell. 2019 Oct;179(3):736-749.e15.
  25. Yu M, Tham YC, Rim TH, Ting DSW, Wong TY, Cheng CY. Reporting on deep learning algorithms in health care. Lancet Digit Health. 2019 Nov;1(7):e328-e329.
  26. Tham YC, Cheng CY, Wong TY. Detection of anaemia from retinal images. Nat Biomed Eng. 2020 Jan;4(1):2-3.
  27. Sabanayagam C, Xu D, Ting DSW, Nusinovici S, Banu R, Hamzah H, Lim C, Tham YC, Cheung CY, Tai ES, Wang YX, Jonas JB, Cheng CY, Lee ML, Hsu W, Wong TY. A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations. Lancet Digit Health. 2020 Jun;2(6):e295-e302.
  28. Nusinovici S, Tham YC, Chak Yan MY, Wei Ting DS, Li J, Sabanayagam C, Wong TY, Cheng CY. Logistic regression was as good as machine learning for predicting major chronic diseases. J Clin Epidemiol. 2020 Jun;122:56-69.
  29. Spracklen CN, Horikoshi M, Kim YJ, Lin K, Bragg F, Moon S, …, Cheng CY, et al. Identification of type 2 diabetes loci in 433,540 East Asian individuals. Nature. 2020 Jun;582(7811):240-245.
  30. Tham YC, Chee ML, Dai W, Lim ZW, Majithia S, Siantar R, Thakur S, Rim T, Cheung CY, Sabanayagam C, Aung T, Wong TY, Cheng CY. Profiles of ganglion cell-inner plexiform layer thickness in a multi-ethnic Asian population: The Singapore Epidemiology of Eye Diseases Study. Ophthalmology. 2020 Aug;127(8):1064-1076.
  31. Rim TH*, Lee G*, Kim Y*, Tham YC*, Lee CJ, Baik SJ, Kim YA, Yu M, Deshmukh M, Lee BK, Park S, Kim HC, Sabayanagam C, Ting DSW, Wang YX, Jonas JB, Kim SS#, Wong TY#, Cheng CY#. Prediction of systemic biomarkers from retinal photographs: Development and validation of deep-learning algorithms. Lancet Digit Health. 2020 Oct;2(10):E526-E536.
  32. Rim TH*, Ryo K*, Tham YC, Kang SW, Ruamviboonsuk P, Bikbov MM, Miyake M, Hao J, Fletcher A, Sasaki M, Nangia V, Sabanayagam C, Yu M, Fujiwara K, Thapa R, Wong IY, Kayama T, Chen SJ, Kuang TM, Yamashita H, Sundaresan P, Chan JC, van Rens GHMB, Sonoda KH, Wang YX, Panda-Jonas S, Harada S, Kim R, Ganesan S, Raman R, Yamashiro K, Gilmanshin TR, Jenchitr W, Park KH, Cheung CMG, Wong TY, Wang N, Jonas JB, Chakravarthy U, Cheng CY#, Yanagi Y#, Asian Eye Epidemiology Consortium. Prevalence and pattern of geographic atrophy in Asia: The Asian Eye Epidemiology Consortium. Ophthalmology. 2020 Oct;127(10):1371-1381.
  33. Yonova-Doing E, Zhao W, Igo RP Jr, Wang C, Sundaresan P, Lee KE, …, Cheng CY. Common variants in SOX-2 and congenital cataract genes contribute to age-related nuclear cataract. Commun Biol. 2020 Dec;3(1):755.
  34. Tham YC, Anees A, Zhang L, Goh JHL, Rim TH, Nusinovici S, Hamzah H, Chee ML, Tjio G, Li S, Xu X, Goh R, Tang F, Cheung CY, Wang YX, Nangia V, Jonas JB, Gopinath B, Mitchell P, Husain R, Lamoureux E, Sabanayagam C, Wang JJ, Aung T, Liu Y, Wong TY, Cheng CY. Referral for disease-related visual impairment using retinal photograph-based deep learning: A proof-of-concept, model development study. Lancet Digit Health. 2021 Jan;3(1):e29-e40.
  35. Gharahkhani P*, Jorgenson E*, Hysi P*, Khawaja AP*, Pendergrass S*, Han X, Ong JS, Hewitt AW, Segrè AV, Rouhana JM, Hamel AR, Igo RP Jr, Choquet H, Qassim A, Josyula NS, Cooke Bailey JN, Bonnemaijer PWM, Iglesias A, Siggs OM, Young TL, Vitart V, Thiadens AAHJ, Karjalainen J, Uebe S, Melles RB, Nair KS, Luben R, Simcoe M, Amersinghe N, Cree AJ, Hohn R, Poplawski A, Chen LJ, Rong SS, Aung T, Vithana EN; NEIGHBORHOOD consortium; ANZRAG consortium; Biobank Japan project; FinnGen study; UK Biobank Eye and Vision Consortium; GIGA study group; 23 and Me Research Team, Tamiya G, Shiga Y, Yamamoto M, Nakazawa T, Currant H, Birney E, Wang X, Auton A, Lupton MK, Martin NG, Ashaye A, Olawoye O, Williams SE, Akafo S, Ramsay M, Hashimoto K, Kamatani Y, Akiyama M, Momozawa Y, Foster PJ, Khaw PT, Morgan JE, Strouthidis NG, Kraft P, Kang JH, Pang CP, Pasutto F, Mitchell P, Lotery AJ, Palotie A, van Duijn C, Haines JL, Hammond C, Pasquale LR, Klaver CCW, Hauser M, Khor CC, Mackey DA, Kubo M, Cheng CY, Craig JE, MacGregor S, Wiggs JL. Genome-wide meta-analysis identifies 127 open-angle glaucoma loci with consistent effect across ancestries. Nat Commun. 2021 Feb 24;12(1):1258.
  36. Chong RS, Chee ML, Tham YC, Majithia S, Thakur S, Teo ZL, Da Soh Z, Chua J, Tan B, Wong DW, Schmetterer L, Sabanayagam C, Cheng CY. Association of antihypertensive medication with retinal nerve fiber layer and ganglion cell-Inner plexiform layer thickness. Ophthalmology. 2021 Mar;128(3):393-400.
  37. Rim TH*, Lee CJ*, Tham YC*, Cheung N, Yu M, Lee G, Kim Y, Ting DSW, Chong CCY, Choi YS, Yoo TK, Ryu IH, Baik SJ, Kim YA, Kim SK, Lee SH, Lee BK, Kang SM, Wong EYM, Kim HC, Kim SS#, Park S#, Cheng CY#, Wong TY#. Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs. Lancet Digit Health. 2021 May;3(5):e306-e316.
  38. Majithia S, Tham YC, Chee ML, Nusinovici S, Teo CL, Chee ML, Thakur S, Soh ZD, Kumari N, Lamoureux E, Sabanayagam C, Wong TY, Cheng CY. Cohort Profile: The Singapore Epidemiology of Eye Diseases study (SEED). Int J Epidemiol. 2021 Mar 3;50(1):41-52.
  39. Soh ZD, Yu M, Betzler BK, Majithia S, Thakur S, Tham YC, Wong TY, Aung T, Friedman DS, Cheng CY. The global extent of undetected glaucoma in adults: A systematic review and meta-analysis. Ophthalmology. 2021 Apr :S0161-6420(21)00277-3. Online ahead of print.
  40. Teo ZL, Tham YC, Yu M, Chee ML, Rim TH, Cheung N, Bikbov MM, Wang YX, Tang Y, Lu Y, Wong IY, Ting DSW, Tan GSW, Jonas JB, Sabanayagam C, Wong TY, Cheng CY. Global prevalence of diabetic retinopathy and projection of burden through 2045: Systematic review and meta-analysis. Ophthalmology. 2021 May:S0161-6420(21)00321-3. Online ahead of print.
  41. Cheung CY, Xu D, Cheng CY, Sabanayagam C, Tham YC, Yu M, Rim TH, Chai CY, Gopinath B, Mitchell P, Poulton R, Moffitt TE, Caspi A, Yam JC, Tham CC, Jonas JB, Wang YX, Song SJ, Burrell LM, Farouque O, Li LJ, Tan GSW, Ting DSW, Hsu W, Lee ML, Wong TY. A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre. Nat Biomed Eng. 2021 Jun;5(6):498-508.



Prof Cheng Ching-Yu

Deputy Head

Assoc Prof Charumathi Sabanayagam

Investigators & Fellows

  • Dr Tham Yih Chung

  • Dr Simon Nusinovici

  • Dr Sahil Thakur

Research Associate / Officer

  • Dr Chen Yanyan

  • Dr Shivani Majithia

  • Soh Zhi Da

  • Peng Qingsheng

  • Quek Ten Cheer

  • Jocelyn Goh Hui Lin

  • He Feng

  • Zann Lee Yan Shin

Data Science Team

  • Dr Marco Yu

  • Chee Miao Li

  • Crystal Chong

  • Mihir Deshmukh

  • Dr Fan Qiao

Research Clinic Team

  • Teo Cong Ling

  • Binu Thapa

  • Kaeley Koh Kai Hui

  • Sarah Tan Shwu Huey

  • Rachel Marjorie Tseng Wei Wen

  • Cheok Kaa Ming

  • Gao Fei

  • Rosesita Binte Shaikh

  • Manivannan Udayaraj

  • Shernisee Chia

Biobank Laboratory

  • Chee Miao Ling

  • Chia Boon Jun

Research Administration

  • Ho Kee Ka

  • Riswana Banu

SNEC Clinical Team

  • Prof Wong Tien Yin

  • Prof Gemmy Cheung Chui Ming

  • Assoc Prof Gavin Tan Siew Wei

  • Dr Kelvin Teo Yi Chong

  • Assoc Prof Danny Cheung Ning

  • Clin Assoc Prof Anna Tan Cheng Sim

  • Dr Beau James Fenner

  • Dr Nicholas Tan Yi Qiang

  • Dr Lim Sing Hui

  • Dr Stanley Poh Shuoh Jieh

  • Dr Teo Zhen Ling

  • Dr Debra Quek Qiao Yun

  • Dr Ryan Lee

SERI Research Team

  • Prof Ecosse Lamoureux

  • Prof Saw Seang Mei

  • Prof Leopold Schmetterer

  • Prof Louis Tong

  • Assoc Prof Zhou Lei

  • Dr Jacqueline Chua Yu Min

  • Dr Ryan Mann

  • Dr Preeti Gupta