Congratulations to the Lions Eye Institute’s Dr Jason Charng, who has been awarded the Ophthalmic Research Institute of Australia (ORIA) Perth Eye Foundation Grant.
The grant will be used to apply machine learning to efficiently analyse fundus autofluorescence images in preparation for gene therapy.
To get to know a little more about Dr Charng and his innovative work, he’s graciously agreed to answer a few questions for us:
What are the benefits of applying machine learning in regard to gene therapy?
Retinitis pigmentosa, which is an inherited eye disease that results in progressive vision loss, was originally thought to be untreatable. However, with advances in medical research, there are clinical trials being planned to treat the disease. In the clinic, we take specialised photos of patients with retinitis pigmentosa, which allows the ophthalmologist to assess the health of the light sensing layer at the back of the eye. Manual marking and quantifying these images takes a long time and uses up precious manpower. With generous support from ORIA we plan to develop a machine learning based platform that will efficiently and accurately analyse these images. More importantly, as treatment options emerge, clinicians need to be able to quickly decide during the consultation whether to continue, modify or abandon treatment, without spending extended time on image processing.
Who will you work with to develop the machine learning based platform?
Associate Professor Fred Chen and Professor David Mackey from the Lions Eye Institute, and Dr David Alonso-Caneiro from Queensland University of Technology.
What has been a particular career highlight for you within the last 5 years?
Having had the opportunity to spend 4 years at the University of Pennsylvania, USA and was a part of the first clinical trial in the world using antisense oligonucleotide to treat retinal degeneration.
What is a skill or talent of yours that most people may not know about?
Not sure if this is a talent but I’ve been changing my newborn’s diapers single-handed. Will work on my non-dominant hand next.