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Predicting children's myopia development based on deep learning multi-modal fusion

Predicting children's myopia development based on deep learning multi-modal fusion

Status
Active, not recruiting
Phases
Early Phase 1
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2000035783
Enrollment
Unknown
Registered
2020-08-16
Start date
2020-10-01
Completion date
Unknown
Last updated
2020-08-17

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Myopia

Interventions

Sponsors

Shanghai General Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
6 Years to 12 Years

Inclusion criteria

Inclusion criteria: Primary school children who have already suffered from myopia, including children treated with atropine (1% atropine once every two weeks), children treated with orthokeratology, and children who do not use any means to relieve the progression of myopia.

Exclusion criteria

Exclusion criteria: Primary school children with other serious eye diseases, such as strabismus, amblyopia, congenital cataract, etc.; best corrected visual acuity in both eyes <5.0; children with serious systemic diseases; other systemic diseases, such as diabetes, leukemia, Marfan syndrome etc..

Design outcomes

Primary

MeasureTime frame
refractvie diptor;

Secondary

MeasureTime frame
axial length;the structure of retina and choroid;

Countries

China

Contacts

Public ContactJin Peiyao

Shanghai General Hospital

peiyaojin@126.com+86 13564534911

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026