Automated Measurement of Ocular Movements Using Deep Learning-Based Image Analysis. (2nd September 2022)

• Record Type: Journal Article
• Authors: Lou, Lixia; Sun, Yiming; Huang, Xingru; Jin, Kai; Tang, Xiajing; Xu, Zhaoyang; Zhang, Qianni; Wang, Yaqi; Ye, Juan
• Is Part Of: Current eye research. Volume 47:Number 9(2022)
• Page Start: 1346
• Page End: 1353
• Publication Date: 2022-09-02
• Subjects: Ocular movements -- automated measurements -- deep learning -- image analysis -- age; Ophthalmology -- Periodicals; Eye -- Diseases -- Periodicals; Ophthalmology -- Periodicals; 573.88
• DOI: 10.1080/02713683.2022.2053165
• Languages: English
• ISSNs: 0271-3683

Abstract

Abstract: Purpose: 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. Methods: 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. Results: The intraclass correlation coefficients between manual and automated measurements of six extraocular muscles ranged from 0.802 to 0.848 ( P < 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 ( P < 0.05). Conclusions: 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.