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NCT Number: NCT07146737

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

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Key information

Conditions

ICL AI (Artificial Intelligence) Cytopenia Deep Learning Hematologic Diseases Hemic and Lymphatic Diseases Immune System Diseases Immunologic Deficiency Syndromes Leukocyte Disorders Leukopenia Lymphopenia T-Lymphocytopenia, Idiopathic CD4-Positive Vault

Age range

18 year–45 year

Sex eligibility

All sexes

Study type

Observational

Primary location

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Corneal tomography generation model after ICL surgery

Diagnostic 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.

Primary outcomes

  1. 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

Secondary outcomes

  1. Sensitivity and specificity of convolutional neural network in predicting Vault after ICL implantation

    Time frame: Day 7

    Sensitivity and specificity of convolutional neural network in predicting Vault after ICL implantation

Study contacts

Contact information is provided by the study sponsor or research team.

Fu F Gui

CONTACT

[email protected]

+8613879101919

Sponsors and collaborators

Lead sponsor

Second Affiliated Hospital of Nanchang University

Other

Registry information

Official study title

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

Important dates

Study start
2025
Primary completion
2025
Study completion
2028
First posted
Aug 28, 2025
Registry last updated
Aug 28, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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