DL Models Predicting Cycloplegic Refractive Error Based on Non-Cycloplegic Parameters in Myopic Adults
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In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Machine learning model for predicting cycloplegic refraction.
- Who it may be relevant to
- Registry conditions: Refractive Errors, Cycloplegic Refraction, Accommodation. Basic parameters: 18 years — 47 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Efficacy of Deep Learning Models for Predicting Cycloplegic Refractive Error Based on Non-Cycloplegic Parameters in Adults With Myopia
Overview
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.
Detailed description
Myopia is a highly prevalent, irreversible refractive disorder with substantial impact on quality of life. Cycloplegic refraction is the gold standard for assessing refractive error in adults considering optical or surgical correction, but it is time-consuming, slow to recover from, and frequently associated with ocular discomfort. Non-cycloplegic refraction is therefore used routinely in clinical practice, despite known differences from cycloplegic values in a subset of adult myopes.
Critically, this discrepancy varies substantially between individuals and cannot be anticipated from non-cycloplegic measurements alone. Clinicians have no reliable way to identify, prior to dilation, which patients are likely to be overcorrected if cycloplegia is omitted, potentially leading to overcorrected prescriptions, asthenopia, and myopic progression.
Machine learning approaches that capture non-linear relationships between clinical predictors and refractive outcomes have shown promise in children, but comparable models for adults remain largely unexplored, and most rely on axial length, which is unavailable in routine optometric settings. Refractive surgery centers offer a uniquely suitable data source, as every candidate undergoes standardized paired non-cycloplegic and cycloplegic refraction with detailed anterior segment biometry during routine preoperative evaluation. This study leverages such data to develop and validate models estimating cycloplegic refractive error from non-cycloplegic parameters, providing a decision-support tool that reduces unnecessary cycloplegia while flagging patients for whom dilated refraction remains indicated.
Interventions
- Diagnostic test 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.
Primary outcome measures
- Accuracy of predicted cycloplegic spherical equivalent [Time frame: Day 0]
Secondary outcome measures (2)
- Diagnostic performance for identifying patients requiring cycloplegic refraction [Time frame: Day 0]
- Agreement between predicted and measured cycloplegic refraction [Time frame: Day 0]
Eligibility criteria
Inclusion criteria
- Age 18 to 60 years, of either sex;
- 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;
- Best-corrected visual acuity of 20/25 or better in each eye;
- Clear cornea, no keratoconus, corneal scarring, or other pathologies; clear lens;
- Intraocular pressure of 21 mmHg or less, with no history of glaucoma;
- No history of ocular surgery, especially corneal refractive surgery or cataract surgery;
- Time interval between non-cycloplegic refraction and cycloplegic refraction of 7 days or less, with complete data.
Exclusion criteria
- Incomplete clinical data to support the diagnosis;
- Ocular conditions such as subclinical keratoconus, keratoconus, or moderate-to-severe corneal haze or leukoma;
- Allergy or contraindication to cycloplegic agents;
- Refusal to participate in the study.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Other
Study locations
China · 1 center
- The Second Affiliated Hospital of Nanchang University, Nanchang, JiangXi 330000 — Jiangxi
Identifiers
NCT: NCT07690813 · [2026] NO.(123)