What's on TV? Detecting age-related neurodegenerative eye disease using eye movement scanpaths

We test the hypothesis that age-related neurodegenerative eye disease can be detected by examining patterns of eye movement recorded whilst a person naturally watches a movie.

Thirty-two elderly people with healthy vision (median age: 70, interquartile range [IQR] 64-75 years) and 44 patients with a clinical diagnosis of glaucoma (median age: 69, IQR 63-77 years) had standard vision examinations including automated perimetry. Disease severity was measured using a standard clinical measure (visual field mean deviation; MD). All study participants viewed three unmodified TV and film clips on a computer set up incorporating the Eyelink 1000 eyetracker (SR Research, Ontario, Canada). Eye movement scanpaths were plotted using novel methods that first filtered the data and then generated saccade density maps. Maps were then subjected to a feature extraction analysis using kernel principal component analysis (KPCA). Features from the KPCA were then classified using a standard machine based classifier trained and tested by a 10-fold cross validation which was repeated 100 times to estimate the confidence interval (CI) of classification sensitivity and specificity.

Patients had a range of disease severity from early to advanced (median [IQR] right eye and left eye MD was -7 [-13 to -5] dB and -9 [-15 to -4] dB, respectively). Average sensitivity for correctly identifying a glaucoma patient at a fixed specificity of 90% was 79% (95% CI: 58-86%). The area under the Receiver Operating Characteristic curve was 0.84 (95% CI: 0.82-0.87).

Huge data from scanpaths of eye movements recorded whilst people freely watch TV type films can be processed into maps that contain a signature of vision loss. In this proof of principle study we have demonstrated that a group of patients with age-related neurodegenerative eye disease can be reasonably well separated from a group of healthy peers by considering these eye movement signatures alone.