Wong, Tien Yin

A common BIM deletion polymorphism mediates intrinsic resistance and inferior responses to tyrosine kinase inhibitors in cancer

Ng, K. P., Hillmer, A. M., Chuah, C. T. H., Juan, W. C., Ko, T. K., Teo, A. S. M., Ariyaratne, P. N., Takahashi, N., Sawada, K., Fei, Y., et al. (2012) · Nature Medicine

Cell death mechanisms and regulationLung Cancer Treatments and Mutations

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Genome-wide meta-analyses of multiancestry cohorts identify multiple new susceptibility loci for refractive error and myopia

Consortium for Refractive Error and Myopia (CREAM), Verhoeven, V. J. M., The Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Research Group, Wellcome Trust Case Control Consortium 2 (WTCCC2), The Fuchs' Genetics Multi-Center Study Group, Hysi, P. G., Wojciechowski, R., Fan, Q., Guggenheim, J. A., Höhn, R., et al. (2013) · Nature Genetics

Ophthalmology and Visual Impairment StudiesGenetic Associations and Epidemiology

Cited by 487

Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study

Bellemo, V., Lim, Z. W., Lim, G., Nguyen, Q. D., Xie, Y., Yip, M. Y. T., Hamzah, H., Ho, J., Lee, X. Q., Hsu, W., et al. (2019) · The Lancet Digital Health

Retinal Imaging and AnalysisRetinal Diseases and Treatments

Cited by 419

Impact of common genetic determinants of hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: A transethnic genome-wide meta-analysis

Wheeler, E., Leong, A., Liu, C.-T., Hivert, M.-F., Strawbridge, R. J., Podmore, C., Li, M., Yao, J., Sim, X., Hong, J., et al. (2017) · PLOS Medicine

Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and Animals

Cited by 408

Prevalence of Age-Related Macular Degeneration in 4 Racial/Ethnic Groups in the Multi-ethnic Study of Atherosclerosis

Klein, R., Klein, B. E. K., Knudtson, M. D., Wong, T. Y., Cotch, M. F., Liu, K., Burke, G., Saad, M. F., & Jacobs, D. R. (2006) · Ophthalmology

Retinal Diseases and TreatmentsRetinal Imaging and Analysis

Cited by 399

Estimated Glomerular Filtration Rate, Albuminuria, and Adverse Outcomes: An Individual-Participant Data Meta-Analysis

Writing Group for the CKD Prognosis Consortium, Appel, L. J., Grams, M., Woodward, M., Harris, K., Arima, H., Chalmers, J., Yatsuya, H., Tamakoshi, K., Li, Y., et al. (2023) · JAMA

Chronic Kidney Disease and DiabetesAcute Kidney Injury Research

Cited by 396

A deep learning system for predicting time to progression of diabetic retinopathy

Dai, L., Sheng, B., Chen, T., Wu, Q., Liu, R., Cai, C., Wu, L., Yang, D., Hamzah, H., Liu, Y., et al. (2024) · Nature Medicine

Retinal Imaging and AnalysisRetinal Diseases and Treatments

Cited by 339

Retinal arteriolar narrowing and risk of diabetes mellitus in middle-aged persons

Wong, T. Y. (2002) · JAMA

Diabetes, Cardiovascular Risks, and LipoproteinsRetinal Diseases and Treatments

Cited by 320

Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs

Liu, H., Li, L., Wormstone, I. M., Qiao, C., Zhang, C., Liu, P., Li, S., Wang, H., Mou, D., Pang, R., et al. (2019) · JAMA Ophthalmology

Retinal Imaging and AnalysisGlaucoma and retinal disorders

Cited by 307

Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls

Platform, B. G., Flannick, J., DiscovEHR Collaboration, CHARGE, LuCamp, ProDiGY, GoT2D, ESP, SIGMA-T2D, T2D-GENES, et al. (2019) · Nature

Genetic Associations and EpidemiologyGenomics and Rare Diseases

Cited by 304

Glycemic Thresholds for Diabetes-Specific Retinopathy

Colagiuri, S., Lee, C. M. Y., Wong, T. Y., Balkau, B., Shaw, J. E., Borch-Johnsen, K., & the DETECT-2 Collaboration Writing Group (2011) · Diabetes Care

Retinal Diseases and TreatmentsDiabetes Management and Research

Cited by 300

Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study

Xie, Y., Nguyen, Q. D., Hamzah, H., Lim, G., Bellemo, V., Gunasekeran, D. V., Yip, M. Y. T., Qi Lee, X., Hsu, W., Li Lee, M., et al. (2020) · The Lancet Digital Health

Retinal Imaging and AnalysisHealth Systems, Economic Evaluations, Quality of Life

Cited by 288

A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations

Sabanayagam, C., Xu, D., Ting, D. S. W., Nusinovici, S., Banu, R., Hamzah, H., Lim, C., Tham, Y.-C., Cheung, C. Y., Tai, E. S., et al. (2020) · The Lancet Digital Health

Retinal Imaging and AnalysisChronic Kidney Disease and Diabetes

Cited by 279

Kidney and eye diseases: Common risk factors, etiological mechanisms, and pathways

Wong, C. W., Wong, T. Y., Cheng, C.-Y., & Sabanayagam, C. (2014) · Kidney International

Chronic Kidney Disease and DiabetesRetinal Diseases and Treatments

Cited by 276

Diabetic Retinopathy and the Risk of Coronary Heart Disease

Cheung, N., Wang, J. J., Klein, R., Couper, D. J., Sharrett, A. R., & Wong, T. Y. (2007) · Diabetes Care

Retinal Diseases and TreatmentsDiabetes, Cardiovascular Risks, and Lipoproteins

Cited by 267

An Automated Grading System for Detection of Vision-Threatening Referable Diabetic Retinopathy on the Basis of Color Fundus Photographs

Li, Z., Keel, S., Liu, C., He, Y., Meng, W., Scheetz, J., Lee, P. Y., Shaw, J., Ting, D., Wong, T. Y., et al. (2018) · Diabetes Care

Retinal Imaging and AnalysisRetinal Diseases and Treatments

Cited by 255

Integrated image-based deep learning and language models for primary diabetes care

Li, J., Guan, Z., Wang, J., Cheung, C. Y., Zheng, Y., Lim, L.-L., Lim, C. C., Ruamviboonsuk, P., Raman, R., Corsino, L., et al. (2024) · Nature Medicine

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 250

Retinal Vascular Changes in Pre-Diabetes and Prehypertension

Nguyen, T. T., Wang, J. J., & Wong, T. Y. (2007) · Diabetes Care

Retinal Imaging and AnalysisRetinal Diseases and Treatments

Cited by 237

Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs

Rim, T. H., Lee, C. J., Tham, Y.-C., Cheung, N., Yu, M., Lee, G., Kim, Y., Ting, D. S. W., Chong, C. C. Y., Choi, Y. S., et al. (2021) · The Lancet Digital Health

Cardiac Imaging and DiagnosticsRetinal Imaging and Analysis

Cited by 234

Cost-effectiveness of a National Telemedicine Diabetic Retinopathy Screening Program in Singapore

Nguyen, H. V., Tan, G. S. W., Tapp, R. J., Mital, S., Ting, D. S. W., Wong, H. T., Tan, C. S., Laude, A., Tai, E. S., Tan, N. C., et al. (2016) · Ophthalmology

Health Systems, Economic Evaluations, Quality of LifeRetinal Diseases and Treatments

Cited by 218

Genome-Wide Association Study Identifies a Novel Locus Contributing to Type 2 Diabetes Susceptibility in Sikhs of Punjabi Origin From India

Saxena, R., Saleheen, D., Been, L. F., Garavito, M. L., Braun, T., Bjonnes, A., Young, R., Ho, W. K., Rasheed, A., Frossard, P., et al. (2013) · Diabetes

Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and Animals

Cited by 185

LLM-based agentic systems in medicine and healthcare

Qiu, J., Lam, K., Li, G., Acharya, A., Wong, T. Y., Darzi, A., Yuan, W., & Topol, E. J. (2024) · Nature Machine Intelligence

Artificial Intelligence in Healthcare and EducationTechnology and Human Factors in Education and Health

Cited by 168

Large language models and their impact in ophthalmology

Betzler, B. K., Chen, H., Cheng, C.-Y., Lee, C. S., Ning, G., Song, S. J., Lee, A. Y., Kawasaki, R., van Wijngaarden, P., Grzybowski, A., et al. (2023) · The Lancet Digital Health

Artificial Intelligence in Healthcare and EducationOphthalmology and Visual Health Research

Cited by 161

Social and emotional impact of diabetic retinopathy: a review

Fenwick, E., Rees, G., Pesudovs, K., Dirani, M., Kawasaki, R., Wong, T. Y., & Lamoureux, E. (2012) · Clinical & Experimental Ophthalmology

Retinal Diseases and TreatmentsDiabetes Management and Education

Cited by 156