Myopia is a common cause of vision loss, being particularly prevalent in children in East and Southeast Asia. The investigators will assess prevalence and incidence of myopia, identify digital biomarkers associated with myopia, and validate algorithms for the detection and/or predition of myopia and other ocular abnormalities in school-aged children in both urban and rural settings in Southern China.
Myopia is a common cause of vision loss, being particularly prevalent in East and Southeast Asia. It is still not entirely clear whether and how visual experience in an urban environment with less outdoor exposure could have an impact on the development and progression of myopia. Zhaoqing has a relatively stable population of 4,084,600, which are representative of the Chinese population in term of demographic and socioeconomic characteristics. Therefore, the investigators will conduct a longitudinal cohort study in both urban and rural settings to examine prevalence and incidence of myopia, identify digital biomarkers associated with myopia, and validate algorithms for the detection and/or predition of incidence and progression of myopia and other ocular abnormalities in school-aged children in Zhaoqing.
Study Type
OBSERVATIONAL
Enrollment
4,000
Ophthalmic examinations include visual acuity, cover test and ocular dominance, noncycloplegic autorefraction, cycloplegia, ocular biometric measurements, cycloplegic auto-refraction, subjective refraction, and anterior and posterior segment examination.
Physical activity, light intensity, and visual information will be measured with wearable devices.
Zhognshan Ophthalmic Center, Sun Yat-sen University
Guangzhou, Guangdong, China
RECRUITINGSchools
Zhaoqing, Guangdong, China
RECRUITINGIncident myopia
Incident myopia is defined as myopia detected during follow up among those without myopia at baseline. Myopia is defined as any eye's SER (sphere + 1/2 cylinder) of at least -0.5 diopters (D).
Time frame: 3 years
Prevalence of myopia
Myopia is defined as any eye's SER (sphere + 1/2 cylinder) of at least -0.5 diopters (D).
Time frame: baseline
Change in axial length
Axial length will be measured with a non-contact optical device.
Time frame: 1 year, 2 years, 3 years
Prevalence of amblyopia, strabismus and other ocular abnormalities
Cover-uncover tests will be performed to detect strabismus. Any ocular abnormalities, including corneal opacities, lens opacities, and retinal diseases will be recorded based on slit lamp, direct ophthalmoscopic and/or mobile phone video examination. Participants with an uncorrected visual acuity 6/7.5 or worse with undergo subjective refraction to identify amblyopia.
Time frame: baseline
Area under the receiver operating characteristic curve of the deep learning algorithm for the prediction of incident myopia
The investigators will estimate the area under the receiver operating characteristic curve of the deep learning algorithm for the prediction of incident myopia.
Time frame: 1 year
Sensitivity and specificity of the deep learning algorithm for the prediction of incident myopia
The investigators will estimate sensitivity and specificity of the deep learning algorithm for the prediction of incident myopia.
Time frame: 1 year
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Area under the receiver operating characteristic curve of the deep learning algorithm for the prediction of fast progressing myope
The investigators will estimate the area under the receiver operating characteristic curve of the deep learning algorithm for the prediction of fast progressing myope (a change in SER of 0.75D or more per year).
Time frame: 1 year
Sensitivity and specificity of the deep learning algorithm for the prediction of fast progressing myope
The investigators will estimate the sensitivity and specificity of the deep learning algorithm for the prediction of fast progressing myope (a change in SER of 0.75D or more per year). Cycloplegic spherical refraction changes measured by an auto-refractometer will be used as the indicator of myopia progression.
Time frame: 1 year
Area under the receiver operating characteristic curve of the diagnostic algorithm in identifying abnormal vision screening result
The investigators will estimate the area under the receiver operating characteristic curve of the diagnostic algorithm in identifying abnormal vision screening result (e.g., abnormal eye lid, abnormal cornea, and strabismus detected with mobile devices).
Time frame: baseline
Sensitivity and specificity of the diagnostic algorithm in identifying abnormal vision screening result
The investigators will estimate the sensitivity and specificity of the diagnostic algorithm in identifying abnormal vision screening result (e.g., abnormal eye lid, abnormal cornea, and strabismus detected with mobile devices).
Time frame: baseline
Post-vision screening referral uptake
Any referral uptake will be confirmed by telephone follow-up.
Time frame: 3 months