Refractive ErrorsNCT07690813
ML Models Predicting Cycloplegic Refractive Error Based on Non-Cycloplegic Parameters in Myopic Adults
An observational study of Refractive Errors and Cycloplegic Refraction, run by Second Affiliated Hospital of Nanchang University at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 47 years
- Conditions the study is for
- Refractive Errors, Cycloplegic Refraction, Accommodation
- What is being tested?
- Machine learning model for predicting cycloplegic refraction
- How many people
- 2,500 participants
- Where
- 1 siteChina
- Study dates
- 3 Oct 2023to 25 Nov 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 47 years
- All sexes
- No healthy volunteers
- Adult
Study population: Each subject underwent a comprehensive preoperative examination, including cycloplegic and non-cycloplegic refractions, Pentacam, etc.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
The Second Affiliated Hospital of Nanchang University, Nanchang, JiangXi 330000
Jiangxi
Jian Xiong, 18170906556, 894040417@qq.com
Fu Gui, 13879101919, 564436578@qq.com
About this study
Official title: Efficacy of Machine Learning Models for Predicting Cycloplegic Refractive Error Based on Non-Cycloplegic Parameters in Adults With Myopia
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.
Read the 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.
- Conditions
- Refractive Errors, Cycloplegic Refraction, Accommodation
- Being tested
- Machine learning model for predicting cycloplegic refraction
- Keywords
- Accommodation, Cycloplegic refraction, Refractive errors
- Medical subject headings
- Refractive Errors
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Machine learning model for predicting cycloplegic refractionDiagnostic test
The machine learning model was applied to each participant's non-cycloplegic parameters to predict cycloplegic spherical equivalent.
| Study group | Role | What they receive |
|---|---|---|
| Group with spherical equivalent change ≥0.50 diopters after cycloplegic refraction | Adult myopes with a non-cycloplegic versus cycloplegic spherical equivalent difference of ≥0.50 diopters, for whom cycloplegic refraction is clinically warranted, received routine cycloplegic refraction with tropicamide; no other intervention was given. Receives: Diagnostic Test: Machine learning model for predicting cycloplegic refraction | |
| Group with spherical equivalent change <0.50 diopters after cycloplegic refraction | Adult myopes with an absolute difference of less than 0.50 diopters between non-cycloplegic and cycloplegic spherical equivalent, for whom non-cycloplegic refraction is considered sufficient, received routine cycloplegic refraction with tropicamide; no additional intervention was applied. Receives: Diagnostic Test: Machine learning model for predicting cycloplegic refraction |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of predicted cycloplegic spherical equivalent
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.
Measured: Day 0
Secondary outcomes (2)
Diagnostic performance for identifying patients requiring cycloplegic refraction
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.
Measured: Day 0
Agreement between predicted and measured cycloplegic refraction
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.
Measured: Day 0
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Jul 2026
- Study start3 Oct 2023
- Primary completion25 Nov 2026 (estimated)
- Study completion25 Nov 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Refractive ErrorsStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Cross-sectional
- Enrolment
- 2,500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Second Affiliated Hospital of Nanchang University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Jian Xiong, Second Affiliated Hospital of Nanchang University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07690813
- Sponsor's protocol ID
- [2026] NO.(123)
- Status last verified
- Jul 2026
- First submitted
- 21 Jun 2026
- Last updated
- 21 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 21 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Efficacy of Machine Learning Models for Predicting Cycloplegic Refractive Error Based on Non-Cycloplegic Parameters in Adults With Myopia
- ClinicalTrials.gov ID
- NCT07690813
- Acronym / sponsor ID
- [2026] NO.(123)
- Conditions
- Refractive Errors, Cycloplegic Refraction, Accommodation
- Keywords
- Accommodation, Cycloplegic refraction, Refractive errors
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 2,500 participants (estimated)
- Lead sponsor
- Second Affiliated Hospital of Nanchang UniversityUniversity, hospital or other
- Study start
- 3 Oct 2023 (actual)
- Primary completion
- 25 Nov 2026 (estimated)
- Study completion
- 25 Nov 2026 (estimated)
- First posted
- 8 Jul 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Other
- Time perspective
- Cross-sectional
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Group with spherical equivalent change ≥0.50 diopters after cycloplegic refraction | – | Diagnostic Test: Machine learning model for predicting cycloplegic refractionAdult myopes with a non-cycloplegic versus cycloplegic spherical equivalent difference of ≥0.50 diopters, for whom cycloplegic refraction is clinically warranted, received routine cycloplegic refraction with tropicamide; no other intervention was given. |
| Group with spherical equivalent change <0.50 diopters after cycloplegic refraction | – | Diagnostic Test: Machine learning model for predicting cycloplegic refractionAdult myopes with an absolute difference of less than 0.50 diopters between non-cycloplegic and cycloplegic spherical equivalent, for whom non-cycloplegic refraction is considered sufficient, received routine cycloplegic refraction with tropicamide; no additional intervention was applied. |
Who may qualify
- Ages
- 18 to 47 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult
- Study population
- Each subject underwent a comprehensive preoperative examination, including cycloplegic and non-cycloplegic refractions, Pentacam, etc.
Key inclusion criteria (7 listed)
- 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;
Key exclusion criteria (4 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Machine learning model for predicting cycloplegic refractionThe machine learning model was applied to each participant's non-cycloplegic parameters to predict cycloplegic spherical equivalent. | Diagnostic test | Group with spherical equivalent change <0.50 diopters after cycloplegic refraction, Group with spherical equivalent change ≥0.50 diopters after cycloplegic refraction |
Primary outcome
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.
2 secondary outcomes
Diagnostic performance for identifying patients requiring cycloplegic refraction
Time frame: Day 0
Agreement between predicted and measured cycloplegic refraction
Time frame: Day 0
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 21 Sep 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Refractive Errors and Cycloplegic Refraction, run by Second Affiliated Hospital of Nanchang University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 7 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 21 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact jian xiong (18170906556, 894040417@qq.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of predicted cycloplegic spherical equivalent (time frame: Day 0). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 21 Sep 2026.