Diagnóstico automático da paralisia do sexto nervo em vídeos de motilidade ocular por classificação de séries temporais e aprendizado profundo

Abstract

The sixth cranial nerve innervates the lateral rectus muscle, which is responsible for left-to-right eye movements. Paralysis of this nerve impairs the lateral rectus muscle’s function, potentially causing headaches, migraines, blurred vision, dizziness, and diplopia (double vision) when attempting to move the eye toward the outer corner. Therefore, early diagnosis of this condition is essential to prevent long-term effects. Since available diagnostic techniques are invasive or expensive, this study proposes an automated method for diagnosing sixth cranial nerve palsy based on classifying time series extracted from eye-tracking data of ophthalmological patients using deep learning models, aiming to assist specialists in the diagnostic process. The proposed method uses the YOLO network to track eye movement accurately and quickly in ophthalmological videos, along with the MediaPipe facial detection model, in a strategy to compensate for eye trajectories, removing occasional head movements and isolating the actual eye movement. In a cross validation experiment, the proposed method achieved a mean sensitivity of 70% across the folds and 75% in the best-trained fold, indicating that our methodology has potential for clinical application.