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Wan-Ling Wong

Other affiliations: University Health System
Bio: Wan-Ling Wong is an academic researcher from National University of Singapore. The author has contributed to research in topics: Population & Visual acuity. The author has an hindex of 27, co-authored 54 publications receiving 4811 citations. Previous affiliations of Wan-Ling Wong include University Health System.

Papers published on a yearly basis

Papers
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Journal ArticleDOI
TL;DR: In this article, the authors did a systematic literature review to identify all population-based studies of age-related macular degeneration published before May, 2013, using retinal photographs and standardised grading classifications.

3,062 citations

Journal ArticleDOI
TL;DR: Atropine 0.01% has minimal side effects compared with atropine at 0.1% and 0.5%, and retains comparable efficacy in controlling myopia progression, and had a negligible effect on accommodation and pupil size and no effect on near visual acuity.

515 citations

Journal ArticleDOI
TL;DR: The prevalence of glaucoma among Malay persons 40 years of age and older in Singapore is 3.4%, comparable to ethnic Chinese people in Singapore and other racial/ethnic groups in Asia.
Abstract: PURPOSE. To assess the prevalence and types of glaucoma in an Asian Malay population.METHODS. The Singapore Malay Eye Study is a population-based, cross-sectional survey that examined 3280 (78.7% response) persons aged 40 to 80 years. Participants underwent a standardized clinical examination including slit-lamp biomicroscopy, Goldmann applanation tonometry, and dilated optic disc assessment. Participants who were suspected to have glaucoma also underwent visual field examination (24-2 SITA standard, Humphrey Visual Field Analyzer II), gonioscopy, and repeat applanation tonometry. Glaucoma was defined according to International Society for Geographical and Epidemiologic Ophthalmology criteria.RESULTS. Of the 3280 participants, 150 (4.6%) had diagnosed glaucoma, giving an age- and sex-standardized prevalence of 3.4% (95% confidence interval [CI], 3.3%-3.5%). The age- and sex-standardized prevalence of primary open-angle glaucoma was 2.5% ( 95% CI, 2.4%-2.6%), primary angle-closure glaucoma 0.12% ( 95% CI, 0.10%-0.14%), and secondary glaucoma 0.61% ( 95% CI, 0.59%-0.63%). Of the 150 glaucoma cases, only 12 (8%) had a previous known history of glaucoma. Twenty-seven (18%) eyes had low vision ( based on best corrected visual acuity logarithm of the minimal angle of resolution [logMAR] > 0.30 to = 1.00).CONCLUSIONS. The prevalence of glaucoma among Malay persons 40 years of age and older in Singapore is 3.4%, comparable to ethnic Chinese people in Singapore and other racial/ethnic groups in Asia. As in Chinese, Caucasians, and African people, primary open-angle glaucoma was the main form of glaucoma in this population. More than 90% of glaucoma cases were previously undetected.

259 citations

Journal ArticleDOI
TL;DR: The prevalence of myopia, astigmatism, and anisometropia was lower, whereas the prevalence of hyperopia was similar, compared with previous reports of similarly aged Singapore Chinese adults.

200 citations

Journal ArticleDOI
TL;DR: This population-based study among Malays showed that diabetes and hyperglycemia are associated with thicker central corneas, independent of age and IOP levels, which may have implications for understanding the relationship between diabetes and glaucoma.

200 citations


Cited by
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Journal ArticleDOI
TL;DR: The global prevalence of primary open-angle glaucoma (POAG) and primary angle-closure glauComa (PACG) and the number of affected people in 2020 and 2040 are examined, disproportionally affecting people residing in Asia and Africa.

4,318 citations

Journal ArticleDOI
Seth Flaxman1, Rupert R A Bourne2, Serge Resnikoff3, Serge Resnikoff4, Peter Ackland5, Tasanee Braithwaite6, Maria V Cicinelli, Aditi Das7, Jost B. Jonas8, Jill E Keeffe9, John H. Kempen10, Janet L Leasher11, Hans Limburg, Kovin Naidoo12, Kovin Naidoo4, Konrad Pesudovs13, Alexander J Silvester, Gretchen A Stevens14, Nina Tahhan4, Nina Tahhan3, Tien Yin Wong15, Hugh R. Taylor16, Rupert R A Bourne2, Aries Arditi, Yaniv Barkana, Banu Bozkurt17, Alain M. Bron, Donald L. Budenz18, Feng Cai, Robert J Casson19, Usha Chakravarthy20, Jaewan Choi, Maria Vittoria Cicinelli, Nathan Congdon20, Reza Dana21, Rakhi Dandona22, Lalit Dandona23, Iva Dekaris, Monte A. Del Monte24, Jenny deva25, Laura E. Dreer26, Leon B. Ellwein27, Marcela Frazier26, Kevin D. Frick28, David S. Friedman28, João M. Furtado29, H. Gao30, Gus Gazzard31, Ronnie George32, Stephen Gichuhi33, Victor H. Gonzalez, Billy R. Hammond34, Mary Elizabeth Hartnett35, Minguang He16, James F. Hejtmancik, Flavio E. Hirai36, John J Huang37, April D. Ingram38, Jonathan C. Javitt28, Jost B. Jonas8, Charlotte E. Joslin39, John H Kempen10, Moncef Khairallah, Rohit C Khanna9, Judy E. Kim40, George N. Lambrou41, Van C. Lansingh, Paolo Lanzetta42, Jennifer I. Lim43, Kaweh Mansouri, Anu A. Mathew44, Alan R. Morse, Beatriz Munoz, David C. Musch24, Vinay Nangia, Maria Palaiou10, Maurizio Battaglia Parodi, Fernando Yaacov Pena, Tunde Peto20, Harry A. Quigley, Murugesan Raju45, Pradeep Y. Ramulu46, Zane Rankin15, Dana Reza21, Alan L. Robin23, Luca Rossetti47, Jinan B. Saaddine46, Mya Sandar15, Janet B. Serle48, Tueng T. Shen23, Rajesh K. Shetty49, Pamela C. Sieving27, Juan Carlos Silva50, Rita S. Sitorus51, Dwight Stambolian52, Gretchen Stevens14, Hugh Taylor16, Jaime Tejedor, James M. Tielsch28, Miltiadis K. Tsilimbaris53, Jan C. van Meurs, Rohit Varma54, Gianni Virgili55, Ya Xing Wang56, Ningli Wang56, Sheila K. West, Peter Wiedemann57, Tien Wong15, Richard Wormald6, Yingfeng Zheng15 
Imperial College London1, Anglia Ruskin University2, University of New South Wales3, Brien Holden Vision Institute4, International Agency for the Prevention of Blindness5, Moorfields Eye Hospital6, York Hospital7, Heidelberg University8, L V Prasad Eye Institute9, Massachusetts Eye and Ear Infirmary10, Nova Southeastern University11, University of KwaZulu-Natal12, National Health and Medical Research Council13, World Health Organization14, National University of Singapore15, University of Melbourne16, Selçuk University17, University of Miami18, University of Adelaide19, Queen's University Belfast20, Harvard University21, The George Institute for Global Health22, University of Washington23, University of Michigan24, Universiti Tunku Abdul Rahman25, University of Alabama at Birmingham26, National Institutes of Health27, Johns Hopkins University28, University of São Paulo29, Henry Ford Health System30, University College London31, Sankara Nethralaya32, University of Nairobi33, University of Georgia34, University of Utah35, Federal University of São Paulo36, Yale University37, Alberta Children's Hospital38, University of Illinois at Chicago39, Medical College of Wisconsin40, Novartis41, University of Udine42, University of Illinois at Urbana–Champaign43, Royal Children's Hospital44, University of Missouri45, Centers for Disease Control and Prevention46, University of Milan47, Icahn School of Medicine at Mount Sinai48, Mayo Clinic49, Pan American Health Organization50, University of Indonesia51, University of Pennsylvania52, University of Crete53, University of Southern California54, University of Florence55, Capital Medical University56, Leipzig University57
TL;DR: A series of regression models were fitted to estimate the proportion of moderate or severe vision impairment and blindness by cause, age, region, and year, and found that world regions varied markedly in the causes of blindness and vision impairment in this age group.

1,909 citations

Journal ArticleDOI
TL;DR: The subcommittee reviewed the prevalence, incidence, risk factors, natural history, morbidity and questionnaires reported in epidemiological studies of dry eye disease and confirmed that prevalence increases with age, however signs showed a greater increase per decade than symptoms.
Abstract: The subcommittee reviewed the prevalence, incidence, risk factors, natural history, morbidity and questionnaires reported in epidemiological studies of dry eye disease (DED). A meta-analysis of published prevalence data estimated the impact of age and sex. Global mapping of prevalence was undertaken. The prevalence of DED ranged from 5 to 50%. The prevalence of signs was higher and more variable than symptoms. There were limited prevalence studies in youth and in populations south of the equator. The meta-analysis confirmed that prevalence increases with age, however signs showed a greater increase per decade than symptoms. Women have a higher prevalence of DED than men, although differences become significant only with age. Risk factors were categorized as modifiable/non-modifiable, and as consistent, probable or inconclusive. Asian ethnicity was a mostly consistent risk factor. The economic burden and impact of DED on vision, quality of life, work productivity, psychological and physical impact of pain, are considerable, particularly costs due to reduced work productivity. Questionnaires used to evaluate DED vary in their utility. Future research should establish the prevalence of disease of varying severity, the incidence in different populations and potential risk factors such as youth and digital device usage. Geospatial mapping might elucidate the impact of climate, environment and socioeconomic factors. Given the limited study of the natural history of treated and untreated DED, this remains an important area for future research.

1,322 citations

Journal ArticleDOI
12 Dec 2017-JAMA
TL;DR: In this evaluation of retinal images from multiethnic cohorts of patients with diabetes, the DLS had high sensitivity and specificity for identifying diabetic retinopathy and related eye diseases.
Abstract: Importance A deep learning system (DLS) is a machine learning technology with potential for screening diabetic retinopathy and related eye diseases. Objective To evaluate the performance of a DLS in detecting referable diabetic retinopathy, vision-threatening diabetic retinopathy, possible glaucoma, and age-related macular degeneration (AMD) in community and clinic-based multiethnic populations with diabetes. Design, Setting, and Participants Diagnostic performance of a DLS for diabetic retinopathy and related eye diseases was evaluated using 494 661 retinal images. A DLS was trained for detecting diabetic retinopathy (using 76 370 images), possible glaucoma (125 189 images), and AMD (72 610 images), and performance of DLS was evaluated for detecting diabetic retinopathy (using 112 648 images), possible glaucoma (71 896 images), and AMD (35 948 images). Training of the DLS was completed in May 2016, and validation of the DLS was completed in May 2017 for detection of referable diabetic retinopathy (moderate nonproliferative diabetic retinopathy or worse) and vision-threatening diabetic retinopathy (severe nonproliferative diabetic retinopathy or worse) using a primary validation data set in the Singapore National Diabetic Retinopathy Screening Program and 10 multiethnic cohorts with diabetes. Exposures Use of a deep learning system. Main Outcomes and Measures Area under the receiver operating characteristic curve (AUC) and sensitivity and specificity of the DLS with professional graders (retinal specialists, general ophthalmologists, trained graders, or optometrists) as the reference standard. Results In the primary validation dataset (n = 14 880 patients; 71 896 images; mean [SD] age, 60.2 [2.2] years; 54.6% men), the prevalence of referable diabetic retinopathy was 3.0%; vision-threatening diabetic retinopathy, 0.6%; possible glaucoma, 0.1%; and AMD, 2.5%. The AUC of the DLS for referable diabetic retinopathy was 0.936 (95% CI, 0.925-0.943), sensitivity was 90.5% (95% CI, 87.3%-93.0%), and specificity was 91.6% (95% CI, 91.0%-92.2%). For vision-threatening diabetic retinopathy, AUC was 0.958 (95% CI, 0.956-0.961), sensitivity was 100% (95% CI, 94.1%-100.0%), and specificity was 91.1% (95% CI, 90.7%-91.4%). For possible glaucoma, AUC was 0.942 (95% CI, 0.929-0.954), sensitivity was 96.4% (95% CI, 81.7%-99.9%), and specificity was 87.2% (95% CI, 86.8%-87.5%). For AMD, AUC was 0.931 (95% CI, 0.928-0.935), sensitivity was 93.2% (95% CI, 91.1%-99.8%), and specificity was 88.7% (95% CI, 88.3%-89.0%). For referable diabetic retinopathy in the 10 additional datasets, AUC range was 0.889 to 0.983 (n = 40 752 images). Conclusions and Relevance In this evaluation of retinal images from multiethnic cohorts of patients with diabetes, the DLS had high sensitivity and specificity for identifying diabetic retinopathy and related eye diseases. Further research is necessary to evaluate the applicability of the DLS in health care settings and the utility of the DLS to improve vision outcomes.

1,309 citations