Automatic Retinal Image Analysis (ARIA) for Stroke Risk Assessment

The Automatic Retinal Image Analysis (ARIA) technology has been licensed to

Health View Bioanalytic Limited 

HVB_logo EN and CN 



 Hong Kong Awards for Industries – Technological Achievement Award 2016



Theory into reality: Hong Kong researchers devise five-minute optical test for stroke risk
Press – SCMP on 6 May 2016

SCMP picture 1

Press – 明報 – 6 May 2016

MingPao picture 1


 Ming Pao 2016-05-07@ARIA


  Press – 明報 – 25 Sept 2015 

Health View Bioanalytic ARIA Ming Pao 2015-09-25 Cleaned



5th Bank of China “Technology Start-up ” Merit Award『香港創新科技及製造業聯合總會』(FITMI) 五屆中銀香港『初創科技』潛能大獎 2015 - 優異獎』

FITMI Award 2015 - certificate





CUHK Newsletter – Jan 2013

ARIA - A glimpse of our body

ARIA - Image for AMD123ARIA - Retinal Images of Dry and Wet AMD


Professor Benny Zee, Associate Director (Graduate Studies) and Professor at the Jockey Club School of Public Health and Primary Care and Head of the Division of Biostatistics, CUHK, has developed an algorithm for an automatic diagnostic system for cerebral vessel conditions and evaluation of the risk of stroke. This grew out of a collaboration with Dr. Jack Lee, a biostatistician with expertise in finance and bioinformatics, and Dr. Qing Li, an ophthalmologist and a PhD student of Professor Zee’s who set out to identify vascular diseases before stroke happens. Up to 80% of all diabetics of over 10 years would develop diabetic retinopathy (DR) which is damage to the retina caused by diabetes mellitus, with a concomitant higher chance of suffering stroke at a later stage. DR screening has become a standard procedure in diabetic care but its effectiveness is hampered by several factors: not enough specialists to administer the screening, human variability in diagnosis, long waiting time for the result, and high cost.
To address these issues, the team had to find a methodology to turn the analogue images of the retina into quantifiable and analyzable data. According to Professor Zee, the first difficulty encountered in the process was the location of the optic disc (the spot where the light-sensitive ganglion cell axons leave the eye to form the optic nerve to the brain, also known as the physiological blind spot). Although methods of locating it already existed, his team developed a new method that best fit their purpose. A greater hurdle, however, laid in the detection of new vessels in the eye whose growth is a sure sign of havoc to come. As new blood vessels are short, irregular and squiggly, the determination of their existence and state of growth eluded all existent automatized technology. Applying pattern recognition skills, the team was able to devise an algorithm which reads pixel by pixel the retinal images and analyze such pixels to come up with measurements on exudates, haemorrhages, new vessels and finally achieve the overall evaluation of retinopathy.
Standard retinal images can be transmitted through the Internet to a server installed with the algorithm and the result is available within a short period of time. The new method is non-invasive and will substantially reduce bias due to human perception as well as cost and time. Initial tests have confirmed its dependability and high accuracy rates. Next, Professor Zee intends to expand the technology and apply it to both diabetes and non-diabetes patients for the early detection of strokes.
The eye is the window to the soul, and so is it to sickness. Usually another human eye is required to judge if any hazard is forthcoming—that is the role of the traditional physician. Professor Zee and his team have devised an algorithm that does the job of the expert eye, so that the tool can benefit a wider population of individuals and make bigger impact on health care in general.

徐仲 鍈教授是中文大学赛马会公共卫生及基层医疗学院副院长兼教授,也是生物统计学部主任。他的最新创意成果是开发出一套演算法,供评估脑血管情况和中风风险的 自动诊断系统之用。与徐教授共同研发的,包括专长财经和生物资讯的生物统计学家李作为博士,以及徐教授指导的博士研究生、致力透过检查血管病变防范中风的 眼科医生李青。


为 此,研究团队戮力寻找方法,把视网膜的模拟影像转换成可量化及可分析的数据。他们遇到的第一个难题,是如何找出视神经盘(俗称「盲点」,感光的神经节细胞 轴突汇集成视神经,并由该处离开眼球连接大脑)的准确位置。现成办法是有的,但团队也研发了一套更切合本身需要的方法。至于最大困难,莫过于侦测眼内增生 的血管,它们一旦出现,就是大祸逼近的先兆。增生的血管都是既短且弯,呈不规则形状,要发现并查出它们的生长情况并不容易。团队使出从杂乱无章的资料中识 别模式的工夫,设计了一套运算法,把视网膜图像分拆为像素单位,逐点分析,以量度眼底渗液、出血和血管增生情况,从而得出视网膜病变的整体评估结果。