Machine learning model for predicting cycloplegic refraction
Machine learning model for predicting cycloplegic refraction: The machine learning model was applied to each participant's non-cycloplegic parameters to predict cycloplegic spherical equivalent.
Study summary
This study presents a machine learning model that predicts cycloplegic refraction in adults with myopia using standard non-cycloplegic eye measurements, aiming to reduce the need for cycloplegic drops while still identifying patients who require them.
Eligibility
Sex
ALL
Min age
18 Years
Max age
47 Years
Healthy volunteers
No
Inclusion Criteria:
1. Age 18 to 60 years, of either sex;
2. Spherical equivalent between -0.50 diopters and -10.00 diopters, with myopia in one or both eyes, and with cylinder of 4.00 diopters or less;
3. Best-corrected visual acuity of 20/25 or better in each eye;
4. Clear cornea, no keratoconus, corneal scarring, or other pathologies; clear lens;
5. Intraocular pressure of 21 mmHg or less, with no history of glaucoma;
6. No history of ocular surgery, especially corneal refractive surgery or cataract surgery;
7. Time interval between non-cycloplegic refraction and cycloplegic refraction of 7 days or less, with complete data.
Exclusion Criteria:
1. Incomplete clinical data to support the diagnosis;
2. Ocular conditions such as subclinical keratoconus, keratoconus, or moderate-to-severe corneal haze or leukoma;
3. Allergy or contraindication to cycloplegic agents;
4. Refusal to participate in the study.
Primary outcome measure(s)
Accuracy of predicted cycloplegic spherical equivalent — Day 0 Accuracy of the machine learning model in predicting cycloplegic spherical equivalent in the validation dataset, evaluated by mean absolute error, root mean square error, and coefficient of determination, expressed for spherical equivalent in diopters.
Trial sites (1)
Facility
City
Region
Status
The Second Affiliated Hospital of Nanchang University, Nanchang, JiangXi 330000
Jiangxi
China
Recruiting
More Second Affiliated Hospital of Nanchang University trials in China
This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
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