Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China.
School of Electronic Engineering and Computer Science, Queen Mary University of London, London, The United Kingdom.
Curr Eye Res. 2022 Sep;47(9):1346-1353. doi: 10.1080/02713683.2022.2053165. Epub 2022 Jul 27.
Clinical assessment of ocular movements is essential for the diagnosis and management of ocular motility disorders. This study aimed to propose a deep learning-based image analysis to automatically measure ocular movements based on photographs and to investigate the relationship between ocular movements and age.
207 healthy volunteers (414 eyes) aged 5-60 years were enrolled in this study. Photographs were taken in the cardinal gaze positions. Ocular movements were manually measured based on a modified limbus test using ImageJ and automatically measured by our deep learning-based image analysis. Correlation analyses and Bland-Altman analyses were conducted to assess the agreement between manual and automated measurements. The relationship between ocular movements and age were analyzed using generalized estimating equations.
The intraclass correlation coefficients between manual and automated measurements of six extraocular muscles ranged from 0.802 to 0.848 ( < 0.001), and the bias ranged from -0.63 mm to 0.71 mm. The average measurements were 8.62 ± 1.07 mm for superior rectus, 7.77 ± 1.24 mm for inferior oblique, 6.99 ± 1.23 mm for lateral rectus, 6.71 ± 1.22 mm for medial rectus, 6.81 ± 1.20 mm for inferior rectus, and 6.63 ± 1.37 mm for superior oblique, respectively. Ocular movements in each cardinal gaze position were negatively related to age ( < 0.05).
The automated measurements of ocular movements using a deep learning-based approach were in excellent agreement with the manual measurements. This new approach allows objective assessment of ocular movements and shows great potential in the diagnosis and management of ocular motility disorders.
眼部运动的临床评估对于眼运动障碍的诊断和治疗至关重要。本研究旨在提出一种基于深度学习的图像分析方法，以便根据照片自动测量眼部运动，并研究眼部运动与年龄之间的关系。
本研究纳入了 207 名年龄在 5-60 岁的健康志愿者（414 只眼）。在主视位拍摄照片。使用 ImageJ 基于改良的巩膜试验手动测量眼部运动，并通过我们基于深度学习的图像分析自动测量。采用相关分析和 Bland-Altman 分析评估手动和自动测量之间的一致性。采用广义估计方程分析眼部运动与年龄的关系。
6 条眼外肌的手动和自动测量的组内相关系数范围为 0.802-0.848（ < 0.001），偏差范围为-0.63-0.71mm。平均测量值为：上直肌 8.62±1.07mm，下斜肌 7.77±1.24mm，外直肌 6.99±1.23mm，内直肌 6.71±1.22mm，下直肌 6.81±1.20mm，上斜肌 6.63±1.37mm。每个主视位的眼部运动与年龄呈负相关（ < 0.05）。
基于深度学习的方法自动测量眼部运动与手动测量具有极好的一致性。这种新方法可实现眼部运动的客观评估，在眼运动障碍的诊断和治疗方面具有很大的潜力。
