NCT07146737

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

Predictive Performance of a Generative Model for Corneal Tomography After Implantable Collamer Lens Implantation

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 818 (estimated)
• Sponsor: Second Affiliated Hospital of Nanchang University · Academic / Other
• Sex: All
• Age: 18 Years – 45 Years
• Healthy volunteers: Not accepted

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.

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)

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	Corneal tomography generation model after ICL surgery	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.

Timeline

• Start date: 2025-07-01
• Primary completion: 2025-07-03
• Completion: 2028-12-31
• First posted: 2025-08-28
• Last updated: 2025-08-28

Locations

1 site across 1 country: China