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About us > Our People > Research Students > Mr. LIU Zhengji
Our People
Mr. Chunghim SO | Mr. Dong LIANG | Mr. Milan RAI | Ms. Jing ZHANG | Ms. Shuwen JIA | Ms. Wing Yan LAU | Ms. Yingkun CUI | Ms Jingfang Jennifer BIAN | Mr Shashi Kant CHAUDHARY | Ms Meng CHENG | Mr Ka Wai Jimmy CHEUNG | Ms Hang I Christie LAM | Ms Hoi Lam LI | Ms Sonal Aswin VYAS | Mr Kin Ken WAN | Ms Biyue GUO | Ms Li PAN | Ms Qi TAN | Mr Mezbah UDDIN | Ms Rong LI | Ms. Yuanyuan LIANG | Ms Anqi LYU | Mr Ying Hon SZE | Ms Qin WANG | Ms Hanyu ZHANG | Ms Wei YANG | Ms Mei ZHAO | Ms Yajing YANG | Ms Fangyu XU | Mr Sung Hei Jimmy TSE | Ms. Seen Hang CHAN | Mr. CHAN Kin Ho | Ms. AYERAKWAH Patience Ansomah | Mr. SO Ching | Mr. CATRAL Kirk Patrick Carreon | Mr. LIU Zhengji | Mr. LU Daqian | Ms. QIU Chunting | Mr. YANG Xiayin

Mr. LIU Zhengji

Mr. LIU Zhengji
PhD Student

ORCiD 0000-0003-0457-3028

Title of project:
Development of a computer-aided diagnosis system for predicting myopic control efficacy

In 2019, World Health Organization (WHO) launched the first World report on vision. It reported that 2.6 billon people were suffering from myopia worldwide, in which 312 million were younger than 19 years old. Optical and pharmaceutical myopic control interventions have been proposed such as interventions using myopic defocus such as Defocus Incorporated Soft Contact (DISC) lens, Defocus Incorporated Multiple Segments (DIMS) lens, orthokeratology and atropine. However, the efficacy of myopic control interventions could be only assessed after 4 - 6 months of usage because the average progression rate of myopia in Hong Kong Chinese schoolchildren was approximately 0.50D per year.  Therefore, an algorithm for early predicting the efficacy of myopic control interventions is needed so as to adjust the treatment plan accordingly. With the incorporation of image processing, statistics and machine learning together with the existing datasets from various clinical trials, patterns in response to different myopic control interventions could be recognized. Thus, the efficacy of myopia control methods could be detected and predicted earlier in order to provide an effective myopic control for schoolchildren. In the current proposal, we aim at developing a computer-aided system for predicting myopic control efficacy.