Predicting ICL postoperative vault and anterior chamber tomography from preoperative corneal scans
Predictive Performance of a Generative Model for Corneal Tomography After Implantable Collamer Lens Implantation
Second Affiliated Hospital of Nanchang University · NCT07146737
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 |
|---|---|
| Enrollment | 818 (estimated) |
| Ages | 18 Years to 45 Years |
| Sex | All |
| Sponsor | Second Affiliated Hospital of Nanchang University (other) |
| Locations | 1 site (Nanchang, Jiangxi) |
| Trial ID | NCT07146737 on ClinicalTrials.gov |
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.
Who should consider this trial
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.
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.
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 performed: 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.
Eligibility criteria
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 this trial is running
Nanchang, Jiangxi
- The Second Affiliated Hospital of Nanchang University — Nanchang, Jiangxi, China (RECRUITING)
Study contacts
- Study coordinator: Fu F Gui
- Email: 564436578@qq.com
- Phone: +8613879101919
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.
Conditions: ICL, Vault, Deep Learning, AI, Implantable Collamer Lens implantation surgery, Corneal tomography, vault, Artificial Intelligence