Predictive Performance of a Generative Model for Corneal Tomography After ICL Implantation

Nanchang University

Status: Enrolling

Conditions: Vault; Deep Learning; ICL; AI (Artificial Intelligence)

Treatments: Diagnostic Test: Corneal tomography generation model after ICL surgery

Study type: Observational

Funder types: Other

Identifiers

NCT07146737

[2025] NO.(86)

Details and patient eligibility

About

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.

Full 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.

Enrollment: 818 estimated patients

Sex: All

Ages: 18 to 45 years old

Volunteers: No Healthy Volunteers

Inclusion and 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.

Trial design

818 participants in 1 patient group

Eyes with ICL surgeries

Description:

Eyes with ICL surgeries which were performed by surgeons with experiences.

Treatment:

Diagnostic Test: Corneal tomography generation model after ICL surgery