Chief Supervisor

Dr Rachel CHUN

Project Title

Synopsis

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.