A deep learning-based image analysis model for automated scoring of horizontal ocular movement disorders

This study proposes a deep learning-based image analysis method for automated scoring of the severity of horizontal ocular movement disorders and evaluates its performance against traditional manual scoring methods.

A total of 2,565 ocular images were prospectively collected from 164 patients with ocular movement disorders and 121 healthy subjects. These images were labeled and used as the training set for the RetinaEye automatic scoring model. Additionally, 184 binocular gaze images (left and right turns) were collected from 92 patients with limited horizontal ocular movement, serving as the test set. Manual and automatic scoring were performed on the test set using ImageJ and RetinaEye, respectively. Furthermore, the consistency and correlation between the two scoring methods were assessed.

RetinaEye successfully identified the centers of both pupils, as well as the positions of the medial and lateral canthi. It also automatically calculated the horizontal ocular movement scores based on the pixel coordinates of these key points. The model demonstrated high accuracy in identifying key points, particularly the lateral canthi. In the test group, manual and automated scoring results showed a high level of consistency and positive correlation among all affected oculi (κ = 0.860, p < 0.001; ρ = 0.897, p < 0.001).

The automatic scoring method based on RetinaEye demonstrated high consistency with manual scoring results. This new method objectively assesses the severity of horizontal ocular movement disorders and holds great potential for diagnosis and treatment selection.