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.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.