Myopia
Conditions
Brief summary
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.
Detailed description
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.
Interventions
DIAGNOSTIC_TESTOphthalmic examinations
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.
DEVICEWearable devices
Physical activity, light intensity, and visual information will be measured with wearable devices.
Sponsors
Sun Yat-sen University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
6 Years to 7 Years
Healthy volunteers
Yes
Inclusion criteria
* All first-grade students from 10 primary schools in urban counties, and from 10 primary schools in rural counties, Zhaoqing city.
Exclusion criteria
* No.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Incident myopia | 3 years | 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). |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Change in axial length | 1 year, 2 years, 3 years | Axial length will be measured with a non-contact optical device. |
| Prevalence of amblyopia, strabismus and other ocular abnormalities | baseline | 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. |
| Area under the receiver operating characteristic curve of the deep learning algorithm for the prediction of incident myopia | 1 year | The investigators will estimate the area under the receiver operating characteristic curve of the deep learning algorithm for the prediction of incident myopia. |
| Sensitivity and specificity of the deep learning algorithm for the prediction of incident myopia | 1 year | The investigators will estimate sensitivity and specificity of the deep learning algorithm for the prediction of incident myopia. |
| Prevalence of myopia | baseline | Myopia is defined as any eye's SER (sphere + 1/2 cylinder) of at least -0.5 diopters (D). |
| Sensitivity and specificity of the deep learning algorithm for the prediction of fast progressing myope | 1 year | 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. |
| Area under the receiver operating characteristic curve of the diagnostic algorithm in identifying abnormal vision screening result | baseline | 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). |
| Sensitivity and specificity of the diagnostic algorithm in identifying abnormal vision screening result | baseline | 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). |
| Post-vision screening referral uptake | 3 months | Any referral uptake will be confirmed by telephone follow-up. |
| Area under the receiver operating characteristic curve of the deep learning algorithm for the prediction of fast progressing myope | 1 year | 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). |
Countries
China
Contacts
Primary ContactYingfeng Zheng, M.D. Ph.D.
Outcome results
None listed