Accommodation, Cycloplegic Refraction, Refractive Errors
Conditions
Keywords
Accommodation, Cycloplegic refraction, Refractive errors
Brief 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.
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_TESTMachine learning model for predicting cycloplegic refraction
The machine learning model was applied to each participant's non-cycloplegic parameters to predict cycloplegic spherical equivalent.
Sponsors
Second Affiliated Hospital of Nanchang University
Study design
Observational model
OTHER
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
18 Years to 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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Diagnostic performance for identifying patients requiring cycloplegic refraction | Day 0 | Area under the receiver operating characteristic curve, sensitivity, and specificity of the model for classifying patients with an absolute difference of 0.50 diopters or more between non-cycloplegic and cycloplegic spherical equivalent in the validation dataset. |
| Agreement between predicted and measured cycloplegic refraction | Day 0 | Agreement between predicted and measured cycloplegic spherical equivalent assessed by Bland-Altman analysis with mean bias and 95% limits of agreement, and by the intraclass correlation coefficient in the validation dataset. |
Countries
China
Contacts
CONTACTjian xiong
CONTACTFu Gui
Outcome results
None listed