Prof. Benny Chung-ying ZEE
徐仲鍈教授

Prof. Benny Chung-ying ZEE
Bsc, Msc (Manit.) PhD (Pitt.)
Professor
bzee@cuhk.edu.hk

Research Links

ORCID: 0000-0002-7238-845X

Scopus Author ID: 7006378172

ResearcherID: G-5606-2017

Loop profile: 300865

Personal website: https://www2.ccrb.cuhk.edu.hk/web/

Academic Appointments

  • Director, Office of Research and Knowledge Transfer Services (ORKTS), CUHK
  • Director, Centre for Clinical Research and Biostatistics

Bio

Prof. Benny Zee is the Director of the Office of Research and Knowledge Transfer Services (ORKTS), Director of the Centre for Clinical Research and Biostatistics (CCRB) and Professor of Jockey Club School of Public Health and Primary Care at the Chinese University of Hong Kong (CUHK). In addition, he holds honorary appointments in the Department of Clinical Oncology and the Department of Statistics of CUHK; he was the Chairperson of the Joint CUHK-NTEC Clinical Research Ethics Committee from 2006-2020. He is also the Director of the Clinical Trials and Biostatistics Lab at the CU Shenzhen Research Institute (SZRI).

 

Professor Zee obtained his PhD in Biostatistics from the University of Pittsburgh, USA, in 1987. He then joined the Canadian Cancer Trials Group (CCTG) as a Senior Biostatistician and faculty member in the Department of Community Health and Epidemiology and the Department of Mathematics and Statistics of Queen’s University Canada from 1987 to 2001. He remains an Adjunct Professor at Queen’s University after joining CUHK and actively promotes international academic activities and collaborations.

 

Professor Zee is interested in multi-centre clinical trials, including statistical methods, data management, and drug and medical device development. He has experience developing efficient data management and “big data analytic” research using advanced computer technology. He also has ample experience working with the industry to design and carry out clinical trials that satisfy both academic interests and industry objectives. He has tremendous experience developing hospital infrastructures such as the Ethics Committee, GCP Centre and Risk-based Quality Assurance to conduct clinical research that meets regulatory and international requirements. He is also active in medical device development, such as “automatic retinal imaging analysis (ARIA)”, to assess the risk of stroke, dementia, diabetes and other chronic diseases. The technology has obtained patents from the USA, China and Taiwan and was commercialised through Health View Bioanalytic Limited with support from the Technology Start-up Support Scheme for Universities (TSSSU) of the Innovation Technology Commission (ITC). In addition, he is also active in bioinformatics research and co-founded Beth Bioinformatics Company Limited. He has published more than 304 international peer-reviewed journal articles. He serves on various committees, including advisory committees for drug development and data & safety monitoring committees for international drug trials.

Research Interest

  • AI-based retinal image analysis for chronic disease management
  • Clinical trials methodology: Design of phase II/III clinical trials with multiple endpoints; interim analysis issues; randomised phase II design for hepatocellular cancer (HCC) trials.
  • Biostatistics Methodology: Quality of life analysis with missing data; survival analysis.
  • Bioinformatics analysis – Prognostic modelling with gene expression.
  • Entrepreneurship development
  • Ethical issues in clinical research.
  • Data management in clinical trials

Recent Funded Research Projects

  • Zee B, Lee J, Lai M “Classification of Autism Spectrum Disorder (ASD) and Global Developmental Delay (GDD) in Young Children using a Machine Learning Approach for Retinal Image Analysis”, ITF Innovation and Technology Support Program (ITSP) [ITS/198/21FP], 2022-11-01 to 2024-10-31
  • Chow J, Zee B, Lau AYL, Au KH, Lau AMC “Radiation-associated neurocognitive impairment in nasopharyngeal cancer (NPC) patients after intensity-modulated radiotherapy (IMRT) and its association with radiation dose and retinal vascular characteristics: a cross-sectional study”, Health Medical Research Funds, 2020-08-01 to 2023-01-31
  • Lin ZX, Zee B, Ching J, Lau A, Zhang H, Xian Y, “Efficacy of uncaria rhynchophylla (Gou-Teng) for patients with mild cognitive impairment: a pilot randomized controlled trial using white matter hyperintensities estimated by ARIA as outcome”, Health Medical Research Funds (HMRF 17180221), 2020-08-01 to 2022-01-31
  • Chong M, Zee B. “Development of objective measure of psychotropic substances abuse using Automatic Retinal Image Analysis (ARIA)”, Beat Drugs Fund Regular Funding Scheme 2018, BD180054, 1 July 2019 to 31 June 2021

Selected Publications

  1. Lou J, Liang W, Cao L, Hu I, Zhao S, Chen Z, Chan RWY, Cheung PPH, Zheng H, Liu C, Li Q, Chong MKC, Zhang Y, Yeoh EK, Chan PK, Zee BCY, Mok CKP, Wang MH. Predictive evolutionary modelling for influenza virus by site-based dynamics of mutations. Nature Communication 2024 Mar 21;15(1):2546.

 

  1. Lui G, Leung HS, Lee J, Wong CK, Li X, Ho M, Wong V, Li T, Ho T, Chan YY, Lee SS, Lee AP, Wong KT, Zee B. An efficient approach to estimate the risk of coronary artery disease for people living with HIV using machine-learning-based retinal image analysis. PLoS One. 2023 Feb 24; 18(2): e0281701. https://doi.org/10.1371/journal.pone.0281701

 

  1. Lai, M.; Lee, J.; Li, X.; Kwok, C.; Chong, M.; Zee, B. Lifestyle Changes Reduced Estimated White Matter Hyperintensities Based on Retinal Image Analysis. J. Environ. Res. Public Health 2023, 20, 3530. https://doi.org/10.3390/ijerph20043530

 

  1. Zee B, Lee J, Lai M, Chee P, Rafferty J, Thomas R, Owens D. “Digital solution for detection of undiagnosed diabetes using machine learning-based retinal image analysis”. BMJ Open Diabetes Res Care. 2022 Dec; 10(6): e002914 https://doi:10.1136/bmjdrc-2022-002914

 

  1. Qu Y, Zhuo Y, Lee J, Huang X, Yang Z, YU H, Zhang J, Yuan W, WU J, Zee B, “Ischemic and haemorrhagic stroke risk estimation using a machine-learning-based retinal image analysis”, Frontiers in Neurology, August 2022. Sec. Stroke, https://doi.org/10.3389/fneur.2022.916966 (Erratum in: Front Neurol. 2023 Jul 27;14:1256958).

 

  1. Shi C, Lee J, Wang G, Dou X, Yuan F, Zee B. Assessment of image quality on color fundus retinal images using the automatic retinal image analysis. Sci Rep. 2022 Jun 21;12(1):10455.

 

  1. Cao L, Lou J, Chan SY, Zheng H, Liu C, Zhao S, Li Q, Mok CKP, Chan RWY, Chong MKC, Wu WKK, Chen Z, Wong ELY, Chan PKS, Zee BCY, Yeoh EK, Wang MH. Rapid evaluation of COVID-19 vaccine effectiveness against symptomatic infection with SARS-CoV-2 variants by analysis of genetic distance. Nature Medicine 2022 Jun 16.

 

  1. Chan DCT, Lam WKJ, Hui EP, Ma BBY, Chan CML, Lee VCT, Cheng SH, Gai W, Jiang P, Wong KCW, Mo F, Zee B, King AD, Le QT, Chan ATC, Chan KCA, Lo YMD. Improved risk stratification of nasopharyngeal cancer by targeted sequencing of Epstein-Barr virus DNA in post-treatment plasma. Ann Oncol. 2022 Aug;33(8):794-803
  2. Qu Y, Lee J, Zhuo Y,  Liu S, Thomas R, Owens D, Zee B. Risk Assessment of CHD Using Retinal Images with Machine Learning Approaches for People with Cardiometabolic Disorders. J. Clin. Med. 2022, 11, 2687.

 

  1. Zee B, Wong Y, Lee J, Fan Y, Zeng J, Lam B, Wong A, Shi L, Lee A, Kwok C, Lai M, Mok V, Lau A, “Machine-learning method for localization of cerebral white matter hyperintensities in healthy adults based on retinal images”, Brain Communications, Volume 3, Issue 3, July 2021, fcab124