Department of Orthoptics, Faculty of Medical Technology, Teikyo University, Itabashi, Tokyo, Japan.
Department of Ophthalmology, School of Medicine, Teikyo University, 2-11-1 Kaga, Itabashi, Tokyo, 173-8605, Japan.
Sci Rep. 2022 May 19;12(1):8501. doi: 10.1038/s41598-022-12630-6.
In the evaluation of smooth pursuit eye movements (SPEMs), recording the stimulus onset time is mandatory. In the laboratory, the stimulus onset time is recorded by electrical signal or programming, and video-oculography (VOG) and the visual stimulus are synchronized. Nevertheless, because the examiner must manually move the fixation target, recording the stimulus onset time is challenging in daily clinical practice. Thus, this study aimed to develop an algorithm for evaluating SPEMs while testing the nine-direction eye movements without recording the stimulus onset time using VOG and deep learning-based object detection (single-shot multibox detector), which can predict the location and types of objects in a single image. The algorithm of peak fitting-based detection correctly classified the directions of target orientation and calculated the latencies and gains within the normal range while testing the nine-direction eye movements in healthy individuals. These findings suggest that the algorithm of peak fitting-based detection has sufficient accuracy for the automatic evaluation of SPEM in clinical settings.
在平滑追随眼动（SPEM）的评估中，记录刺激起始时间是强制性的。在实验室中，通过电信号或编程记录刺激起始时间，并使视频眼动图（VOG）和视觉刺激同步。然而，由于检查者必须手动移动注视目标，因此在日常临床实践中记录刺激起始时间具有挑战性。因此，本研究旨在开发一种算法，该算法使用 VOG 和基于深度学习的目标检测（单次多框检测）来评估在不记录刺激起始时间的情况下进行的九方向眼动测试中的 SPEM，单次多框检测可以预测单个图像中物体的位置和类型。基于峰拟合的检测算法在对健康个体进行九方向眼动测试时，可以正确分类目标方向，并在正常范围内计算潜伏期和增益。这些发现表明，基于峰拟合的检测算法在临床环境中具有足够的准确性，可用于 SPEM 的自动评估。
