  Visual perimetry is an important eye examination that helps detect vision
problems caused by ocular or neurological conditions. During the test, a
patient's gaze is fixed at a specific location while light stimuli of varying
intensities are presented in central and peripheral vision. Based on the
patient's responses to the stimuli, the visual field mapping and sensitivity
are determined. However, maintaining high levels of concentration throughout
the test can be challenging for patients, leading to increased examination
times and decreased accuracy.
  In this work, we present RLPeri, a reinforcement learning-based approach to
optimize visual perimetry testing. By determining the optimal sequence of
locations and initial stimulus values, we aim to reduce the examination time
without compromising accuracy. Additionally, we incorporate reward shaping
techniques to further improve the testing performance. To monitor the patient's
responses over time during testing, we represent the test's state as a pair of
3D matrices. We apply two different convolutional kernels to extract spatial
features across locations as well as features across different stimulus values
for each location. Through experiments, we demonstrate that our approach
results in a 10-20% reduction in examination time while maintaining the
accuracy as compared to state-of-the-art methods. With the presented approach,
we aim to make visual perimetry testing more efficient and patient-friendly,
while still providing accurate results.
