Automatic Recording of the Target Location During Smooth Pursuit Eye Movement Testing Using Video-Oculography and Deep Learning-Based Object Detection

Purpose: To accurately record the movements of a hand-held target together with the smooth pursuit eye movements (SPEMs) elicited with video-oculography (VOG) combined with deep learning-based object detection using a single-shot multibox detector (SSD). Methods: The SPEMs of 11 healthy volunteers (21.3 +/- 0.9 years) were recorded using VOG. The subjects fixated on a moving target that was manually moved at a distance of 1 m by the examiner. An automatic recording system was developed using SSD to predict the type and location of objects in a single image. The 400 images that were taken of one subject using a VOG scene camera were distributed into 2 groups (300 and 100) for training and validation. The testing data included 1100 images of all subjects (100 images/subject). The method achieved 75% average precision (AP75) for the relationship between the location of the fixated target (as calculated by SSD) and the position of each eye (as recorded by VOG). Results: The AP75 for all subjects was 99.7% +/- 0.6%. The horizontal and vertical target locations were significantly and positively correlated with each eye position in the horizontal and vertical directions (adjusted R2 >= 0.955, P < 0.001). Conclusions: The addition of SSD-driven recording of hand-held target positions with VOG allows for quantitative assessment of SPEMs following a target during an SPEM test. Translational Relevance: The combined methods of VOG and SSD can be used to detect SPEMs with greater accuracy, which can improve the outcome of clinical evaluations.

Automated summary

The combination of VOG and SSD allows for accurate recording of hand-held target movements and SPEMs and quantitative assessment in eye tracking tests.