Advanced Machine Learning Analytics for Eye-Tracking Based Diagnostic System in Vision Therapy

Amblyopia, also called lazy eye, is a significant visual problem in childre n worldwide. Lack of proper visual development during the first 8 years after birth leads to deficits in sensory and perceptive stimuli processing. Around 5% of children in the world are commonly affected with amblyopia. When left untreated, amblyopia affects everyday activities, quality of life and leads to major learning difficulties. Thus, targeted vision disorder screening is essential for early diagnosis and treatment. The cost of existing diagnostic tools prevents diagnosis and treatment of amblyopia for many in the general population. In the proposed research, the applicants partner with Vision Therapy Research Center in Windsor, Ontario to develop diagnostic tools that are powered by machine learning and artificial intelligence. The proposed research aims to utilize commercially available low-cost eye-tracking devices to develop a diagnostic tool. Particularly, the proposed work will develop machine learning tools to analyze eye-tracking data for diagnosis. The outcome of the proposed research will help to reduce the diagnostic cost of Amblyopia and will lead to its widespread screening and treatment.

Faculty Supervisor:

Balakumar Balasingam

Student:

Partner:

Pediatric and Family Eye Care

Discipline:

Engineering

Sector:

Health and Related Sciences & Technology

University:

University of Windsor

Program: