  Abnormalities in pupillary light reflex can indicate optic nerve disorders
that may lead to permanent visual loss if not diagnosed in an early stage. In
this study, we focus on relative afferent pupillary defect (RAPD), which is
based on the difference between the reactions of the eyes when they are exposed
to light stimuli. Incumbent RAPD assessment methods are based on subjective
practices that can lead to unreliable measurements. To eliminate subjectivity
and obtain reliable measurements, we introduced an automated framework to
detect RAPD. For validation, we conducted a clinical study with
lab-on-a-headset, which can perform automated light reflex test. In addition to
benchmarking handcrafted algorithms, we proposed a transfer learning-based
approach that transformed a deep learning-based generic object recognition
algorithm into a pupil detector. Based on the conducted experiments, proposed
algorithm RAPDNet can achieve a sensitivity and a specificity of 90.6% over 64
test cases in a balanced set, which corresponds to an AUC of 0.929 in ROC
analysis. According to our benchmark with three handcrafted algorithms and nine
performance metrics, RAPDNet outperforms all other algorithms in every
performance category.
