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NCT Number: NCT07690813

DL Models Predicting Cycloplegic Refractive Error Based on Non-Cycloplegic Parameters in Myopic Adults

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.

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Key information

Age range

18 year–47 year

Sex eligibility

All sexes

Study type

Observational

Primary location

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Machine learning model for predicting cycloplegic refraction

Diagnostic Test

The machine learning model was applied to each participant's non-cycloplegic parameters to predict cycloplegic spherical equivalent.

Primary outcomes

  1. Accuracy of predicted cycloplegic spherical equivalent

    Time frame: 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 outcomes

  1. Diagnostic performance for identifying patients requiring cycloplegic refraction

    Time frame: 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.

  2. Agreement between predicted and measured cycloplegic refraction

    Time frame: 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.

Study contacts

Contact information is provided by the study sponsor or research team.

Fu Gui

CONTACT

[email protected]

1387910191

jian xiong

CONTACT

[email protected]

18170906556

Sponsors and collaborators

Lead sponsor

Second Affiliated Hospital of Nanchang University

Other

Registry information

Official study title

Efficacy of Deep Learning Models for Predicting Cycloplegic Refractive Error Based on Non-Cycloplegic Parameters in Adults With Myopia

Important dates

Study start
2023
Primary completion
2026
Study completion
2026
First posted
Jul 8, 2026
Registry last updated
Jul 9, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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