ICLNCT07146737
Predictive Performance of a Generative Model for Corneal Tomography After ICL Implantation
An observational study of ICL and Vault, run by Second Affiliated Hospital of Nanchang University at 1 site in China.
Trial summary
To evaluate the efficacy of a corneal tomography Imaging model in predicting postoperative vault based on preoperative corneal topography in Implantable Collamer Lens (ICL) surgery. Read more about this study
- Who is it for?
- Ages 18 to 45 years
- Conditions the study is for
- ICL, Vault, Deep Learning
- What is being tested?
- Corneal tomography generation model after ICL surgery
- How many people
- 818 participants
- Where
- 1 siteChina
- Study dates
- 1 Jul 2025to 31 Dec 2028
- 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 45 years
- All sexes
- No healthy volunteers
- Adult
Study population: Patients from clinics in different eye centers across China. Each subject must with complete surgical video recording and medical records.
Inclusion Criteria:(1) stable myopia (≤0.50D/year change for 2 years), (2) ACD ≥2.80mm, (3) intact corneal endothelium (≥2000 cells/mm²), and (4) no confounding ocular/systemic conditions.
Exclusion Criteria:(1) glaucoma-spectrum disorders or retinal vasculopathies, (2) prior corneal/intraocular surgery, (3) compromised corneal endothelium, (4) uncontrolled systemic diseases, and (5) pregnancy/lactation.
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
Fu Gui, doctor, +8613879101919, 564436578@qq.com
Jian Xiong, doctor, +8618170906556, 894040417@qq.com
About this study
Official title: Predictive Performance of a Generative Model for Corneal Tomography After Implantable Collamer Lens Implantation
To evaluate the efficacy of a corneal tomography Imaging model in predicting postoperative vault based on preoperative corneal topography in Implantable Collamer Lens (ICL) surgery.
Read the detailed description
Accurate vault prediction is crucial for Implantable Collamer Lens (ICL) surgery safety and efficacy. Current methods using preoperative biometrics and regression formulas show limited accuracy due to parameter variability and incomplete utilization of corneal topography data. To address this, we developed a deep learning model that predicts postoperative vault while generating anterior chamber morphology images from preoperative data, enabling personalized surgical planning.
- Conditions
- ICL, Vault, Deep Learning, AI (Artificial Intelligence)
- Being tested
- Corneal tomography generation model after ICL surgery
- Keywords
- Implantable Collamer Lens (ICL) implantation surgery, Corneal tomography, vault, Artificial Intelligence, Deep Learning
- Medical subject headings
- T-Lymphocytopenia, Idiopathic CD4-Positive
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Corneal tomography generation model after ICL surgeryDiagnostic test
The ICL procedures collected would be assessed by the corneal tomography generation model. The performance of the model would be assessed, including accuracy,AUC, sensitivity and specificity.
| Study group | Role | What they receive |
|---|---|---|
| Eyes with ICL surgeries | Eyes with ICL surgeries which were performed by surgeons with experiences. Receives: Diagnostic Test: Corneal tomography generation model after ICL surgery |
What is being measured
The primary outcome is the main question the study is designed to answer.
AUROC of convolutional neural network in predicting vault after ICL surgery
The area under the receiver operating characteristic of convolutional neural network in predicting vault after ICL surgery
Measured: Day 7
Secondary outcomes (1)
Sensitivity and specificity of convolutional neural network in predicting Vault after ICL implantation
Sensitivity and specificity of convolutional neural network in predicting Vault after ICL implantation
Measured: Day 7
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Aug 2025
- Study start1 Jul 2025
- Primary completion3 Jul 2025
- Study completion31 Dec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for ICLStudies 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
- 818 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Second Affiliated Hospital of Nanchang University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Fu Gui, Second Affiliated Hospital of Nanchang University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07146737
- Sponsor's protocol ID
- [2025] NO.(86)
- Status last verified
- Aug 2025
- First submitted
- 21 Aug 2025
- Last updated
- 28 Aug 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 28 Aug 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Predictive Performance of a Generative Model for Corneal Tomography After Implantable Collamer Lens Implantation
- ClinicalTrials.gov ID
- NCT07146737
- Acronym / sponsor ID
- [2025] NO.(86)
- Conditions
- ICL, Vault, Deep Learning, AI (Artificial Intelligence)
- Keywords
- Implantable Collamer Lens (ICL) implantation surgery, Corneal tomography, vault, Artificial Intelligence, Deep Learning
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 818 participants (estimated)
- Lead sponsor
- Second Affiliated Hospital of Nanchang UniversityUniversity, hospital or other
- Study start
- 1 Jul 2025 (actual)
- Primary completion
- 3 Jul 2025 (actual)
- Study completion
- 31 Dec 2028 (estimated)
- First posted
- 28 Aug 2025 (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.
To evaluate the efficacy of a corneal tomography Imaging model in predicting postoperative vault based on preoperative corneal topography in Implantable Collamer Lens (ICL) surgery.
Detailed description
Accurate vault prediction is crucial for Implantable Collamer Lens (ICL) surgery safety and efficacy. Current methods using preoperative biometrics and regression formulas show limited accuracy due to parameter variability and incomplete utilization of corneal topography data. To address this, we developed a deep learning model that predicts postoperative vault while generating anterior chamber morphology images from preoperative data, enabling personalized surgical planning.
Study design
- Observational model
- Other
- Time perspective
- Cross-sectional
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Eyes with ICL surgeries | – | Diagnostic Test: Corneal tomography generation model after ICL surgeryEyes with ICL surgeries which were performed by surgeons with experiences. |
Who may qualify
- Ages
- 18 to 45 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult
- Study population
- Patients from clinics in different eye centers across China. Each subject must with complete surgical video recording and medical records.
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 |
|---|---|---|
| Corneal tomography generation model after ICL surgeryThe ICL procedures collected would be assessed by the corneal tomography generation model. The performance of the model would be assessed, including accuracy,AUC, sensitivity and specificity. | Diagnostic test | Eyes with ICL surgeries |
Primary outcome
AUROC of convolutional neural network in predicting vault after ICL surgery
Time frame: Day 7
The area under the receiver operating characteristic of convolutional neural network in predicting vault after ICL surgery
1 secondary outcome
Sensitivity and specificity of convolutional neural network in predicting Vault after ICL implantation
Time frame: Day 7
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 28 Aug 2025.
Related trials for ICL
RecruitingPhase 1/2
A Single-arm, Dose-escalation Trial of Long-acting Recombinant Human IL-7 (NT-I7, Efineptakin Alfa) for Idiopathic CD4 Lymphopenia
Idiopathic CD4 Lymphopenia
RecruitingPhase 1
Safety of Belimumab in People With Idiopathic CD4 Lymphopenia and Autoantibodies (Phoebe)
Idiopathic CD4 Lymphopenia
Not yet recruiting
Study of a Phakic Implantable Collamer Lens (ICL)
Myopia, Astigmatism
Not yet recruiting
Study of an Implantable Collamer Lens (ICL)
Myopia, Astigmatism
Recruiting
Safety and Efficacy Study of A Viscoelastic-free Method for Implantable Collamer Lens (ICL) Implantation
Myopia
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 ICL and Vault, 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?
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 28 Aug 2025. 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 Fu F Gui (+8613879101919, 564436578@qq.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
AUROC of convolutional neural network in predicting vault after ICL surgery (time frame: Day 7). 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 28 Aug 2025.