He, Mingguang

Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs

Li, Z., He, Y., Keel, S., Meng, W., Chang, R. T., & He, M. (2018) · Ophthalmology

Retinal Imaging and AnalysisGlaucoma and retinal disorders

Cited by 830

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

Effect of providing free glasses on children's educational outcomes in China: cluster randomized controlled trial

Ma, X., Zhou, Z., Yi, H., Pang, X., Shi, Y., Chen, Q., Meltzer, M. E., le Cessie, S., He, M., Rozelle, S., et al. (2014) · BMJ

Ophthalmology and Visual Impairment StudiesChild Nutrition and Water Access

Cited by 238

Feasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology outpatient services: a pilot study

Keel, S., Lee, P. Y., Scheetz, J., Li, Z., Kotowicz, M. A., MacIsaac, R. J., & He, M. (2018) · Scientific Reports

Retinal Imaging and AnalysisRetinal Diseases and Treatments

Cited by 220

Genetic and environmental influences on height from infancy to early adulthood: an individual-based pooled analysis of 45 twin cohorts

Jelenkovic, A., Sund, R., Hur, Y.-M., Yokoyama, Y., Hjelmborg, J. V. B., Möller, S., Honda, C., Magnusson, P. K. E., Pedersen, N. L., Ooki, S., et al. (2016) · Scientific Reports

Genetic Associations and EpidemiologyGenetics and Physical Performance

Cited by 202

The Association between Vision Impairment and Incidence of Dementia and Cognitive Impairment

Shang, X., Zhu, Z., Wang, W., Ha, J., & He, M. (2021) · Ophthalmology

Dementia and Cognitive Impairment ResearchOphthalmology and Visual Impairment Studies

Cited by 174

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

Psychosocial Factors Affecting Artificial Intelligence Adoption in Health Care in China: Cross-Sectional Study

Ye, T., Xue, J., He, M., Gu, J., Lin, H., Xu, B., & Cheng, Y. (2019) · Journal of Medical Internet Research

Technology Adoption and User BehaviourArtificial Intelligence in Healthcare and Education

Cited by 141

Visualizing Deep Learning Models for the Detection of Referable Diabetic Retinopathy and Glaucoma

Keel, S., Wu, J., Lee, P. Y., Scheetz, J., & He, M. (2019) · JAMA Ophthalmology

Retinal Imaging and AnalysisOphthalmology and Visual Health Research

Cited by 114

Real-world artificial intelligence-based opportunistic screening for diabetic retinopathy in endocrinology and indigenous healthcare settings in Australia

Scheetz, J., Koca, D., McGuinness, M., Holloway, E., Tan, Z., Zhu, Z., O’Day, R., Sandhu, S., MacIsaac, R. J., Gilfillan, C., et al. (2021) · Scientific Reports

Retinal Imaging and AnalysisOphthalmology and Visual Health Research

Cited by 88

Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case

Wang, Y., Liu, C., Hu, W., Luo, L., Shi, D., Zhang, J., Yin, Q., Zhang, L., Han, X., & He, M. (2024) · npj Digital Medicine

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

Cited by 76

A Peer-to-Peer Live-Streaming Intervention for Children During COVID-19 Homeschooling to Promote Physical Activity and Reduce Anxiety and Eye Strain: Cluster Randomized Controlled Trial

Zheng, Y., Wang, W., Zhong, Y., Wu, F., Zhu, Z., Tham, Y.-C., Lamoureux, E., Xiao, L., Zhu, E., Liu, H., et al. (2021) · Journal of Medical Internet Research

COVID-19 and Mental HealthPhysical Activity and Health

Cited by 73

FFA-GPT: an automated pipeline for fundus fluorescein angiography interpretation and question-answer

Chen, X., Zhang, W., Xu, P., Zhao, Z., Zheng, Y., Shi, D., & He, M. (2024) · npj Digital Medicine

Retinal Imaging and AnalysisArtificial Intelligence in Healthcare and Education

Cited by 72

Diagnostic Accuracy of Artificial Intelligence-Based Automated Diabetic Retinopathy Screening in Real-World Settings: A Systematic Review and Meta-Analysis

JOSEPH, S., SELVARAJ, J., MANI, I., KUMARAGURUPARI, T., SHANG, X., MUDGIL, P., RAVILLA, T., & HE, M. (2024) · American Journal of Ophthalmology

Retinal Imaging and AnalysisRetinal Diseases and Treatments

Cited by 69

Associations of ophthalmic and systemic conditions with incident dementia in the UK Biobank

Shang, X., Zhu, Z., Huang, Y., Zhang, X., Wang, W., Shi, D., Jiang, Y., Yang, X., & He, M. (2023) · British Journal of Ophthalmology

Dementia and Cognitive Impairment ResearchRetinal Diseases and Treatments

Cited by 56

Population impact and cost-effectiveness of artificial intelligence-based diabetic retinopathy screening in people living with diabetes in Australia: a cost effectiveness analysis

Hu, W., Joseph, S., Li, R., Woods, E., Sun, J., Shen, M., Jan, C. L., Zhu, Z., He, M., & Zhang, L. (2024) · eClinicalMedicine

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

Cited by 47

Predicting the Development of Type 2 Diabetes in a Large Australian Cohort Using Machine-Learning Techniques: Longitudinal Survey Study

Zhang, L., Shang, X., Sreedharan, S., Yan, X., Liu, J., Keel, S., Wu, J., Peng, W., & He, M. (2020) · JMIR Medical Informatics

Artificial Intelligence in HealthcareDiabetes, Cardiovascular Risks, and Lipoproteins

Cited by 40

Contrastive pre-training and linear interaction attention-based transformer for universal medical reports generation

Lin, Z., Zhang, D., Shi, D., Xu, R., Tao, Q., Wu, L., He, M., & Ge, Z. (2023) · Journal of Biomedical Informatics

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 39

Use of artificial intelligence with retinal imaging in screening for diabetes-associated complications: systematic review

Yang, Q., Bee, Y. M., Lim, C. C., Sabanayagam, C., Yim-Lui Cheung, C., Wong, T. Y., Ting, D. S. W., Lim, L.-L., Li, H., He, M., et al. (2025) · eClinicalMedicine

Retinal Imaging and AnalysisArtificial Intelligence in Healthcare

Cited by 38

ICGA-GPT: report generation and question answering for indocyanine green angiography images

Chen, X., Zhang, W., Zhao, Z., Xu, P., Zheng, Y., Shi, D., & He, M. (2024) · British Journal of Ophthalmology

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 36

Leading mediators of sex differences in the incidence of dementia in community-dwelling adults in the UK Biobank: a retrospective cohort study

Shang, X., Roccati, E., Zhu, Z., Kiburg, K., Wang, W., Huang, Y., Zhang, X., Zhang, X., Liu, J., Tang, S., et al. (2023) · Alzheimer's Research & Therapy

Dementia and Cognitive Impairment ResearchSex and Gender in Healthcare

Cited by 18