Predicting ICL postoperative vault and anterior chamber tomography from preoperative corneal scans
Official title: Predictive Performance of a Generative Model for Corneal Tomography After Implantable Collamer Lens Implantation
Run by Second Affiliated Hospital of Nanchang University · NCT07146737 · Data updated 2026-11-12
This will see if an AI model can use preoperative corneal topography to predict postoperative ICL vault and generate anterior chamber images for people planning ICL surgery.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 18 Years to 45 Years |
| Sex | All |
| Enrollment | 818 (estimated) |
| Sponsor | Second Affiliated Hospital of Nanchang University Academic / other |
| Locations | 1 site (Nanchang, Jiangxi) |
| Status | Recruiting |
| Started | July 1, 2025 |
| Main results expected | July 3, 2025 |
| Study ends | December 31, 2028 |
| Registry record updated | August 28, 2025 |
| Trial ID | NCT07146737 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Ideal candidates are adults with stable myopia (≤0.50 D/year for 2 years), anterior chamber depth ≥2.80 mm, corneal endothelial cell density ≥2000 cells/mm², and no confounding ocular or systemic conditions.
✕Probably not a fit
Patients with glaucoma-spectrum disorders, retinal vasculopathies, prior corneal or intraocular surgery, compromised corneal endothelium, uncontrolled systemic disease, or who are pregnant or lactating are unlikely to benefit or be eligible.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This observational project uses a deep learning generative model trained on preoperative corneal tomography and standard biometric data to predict postoperative vault and synthesize anterior chamber morphology images after ICL implantation. The model takes pre-op scans as input and outputs a vault prediction plus generated tomographic images intended to aid surgical planning. Eligible participants are patients meeting ICL candidacy criteria who provide imaging data; no investigational interventions are performed on patients. The approach aims to leverage more corneal topography information than traditional regression formulas to improve personalized sizing decisions.
Why it matters
Potential benefit: If successful, the tool could help surgeons choose the optimal ICL size, reduce vault-related complications, and make surgical planning more personalized.
How similar studies have gone: Prior deep learning work has shown promise for predicting ICL vault and assisting ICL sizing, but using generative corneal tomography images specifically for vault prediction is relatively novel.
Who can join: the full rules
Show full inclusion / exclusion criteria
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.
Where it's running
-
The Second Affiliated Hospital of Nanchang University
Nanchang, Jiangxi, China Recruiting
Study contacts
- Study coordinator: Fu F Gui
- Email: 564436578@qq.com
- Phone: +8613879101919
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.