Study on the Diagnostic Efficacy of ICL Selection and Prediction Depth Model Based on Eye Images

Recruiting

• Conditions: Posterior Chamber Phakic Intraocular Lens; Vault; Deep Neural Network; Myopia; Anterior Chamber Angle

• Registration Number: NCT06669728
• Lead Sponsor: Second Affiliated Hospital of Nanchang University

Brief Summary

To evaluate the diagnostic efficacy of deep learning network model in implantable collamer lens selection and prediction in a multicenter cross-sectional study

Detailed Description

Posterior chamber intraocular lens implantation is an main choice for myopia correction. Implantable collamer lens (ICL) is currently the most widely used, and the official reference index is mainly based on biological parameters obtained from eye images. The parameter acquisition and selection of ICL design are often controversial, forcing the doctors to synthesize multiple modal data, making the optimization of ICL formula being a focus of attention in refractive surgery. This research aimed to build an image-based ICL prediction algorithm to assist human physicians in decision-making and improve the accuracy, safety and predictability of ICL implantation.

Study Timeline

• Study Start: 2021-01-02
• Study First Submitted: 2024-10-30
• Study First Submitted QC: 2024-10-30
• Study First Posted: 2024-11-01
• Status Verified: 2025-01
• Last Update Submitted: 2025-01-13
• Last Update Posted: 2025-01-15
• Primary Completion: 2025-08-01
• Study Completion: 2025-08-01

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 326

Inclusion Criteria

1. Aged 18-45 years ;
2. Myopia, with or without astigmatism, annual diopter change ≤ 0.50 D for 2 consecutive years ;
3. Anterior chamber depth ≥ 2.80 mm ;
4. Corneal endothelial cell count ≥ 2000 / mm2, stable cell morphology ;
5. There were no other ocular diseases that significantly affected vision and / or systemic organic lesions that affected surgical recovery.

Exclusion Criteria

1. There were no other ocular diseases that significantly affected vision and / or systemic organic lesions that affected surgical recovery;
2. Have a history of corneal refractive surgery or intraocular surgery ;
3. Corneal endothelial cell count is low ;
4. Those with systemic diseases ;
5. Lactating or pregnant women.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
AUROC of convolutional neural network in predicting vault after ICL surgery	Day 7	The area under the receiver operating characteristic of convolutional neural network in predicting vault after ICL surgery
AUROC of convolutional neural network in predicting anterior chamber angle after ICL implantation	Day 7	The area under the receiver operating characteristic of convolutional neural network in predicting anterior chamber angle after ICL implantation

Secondary Outcome Measures

Name	Time	Method
Sensitivity and specificity of convolutional neural network in predicting Vault after ICL implantation	Day 7	Sensitivity and specificity of convolutional neural network in predicting Vault after ICL implantation
Sensitivity and specificity of convolutional neural network in predicting anterior chamber angle after ICL implantation	Day 7	Sensitivity and specificity of convolutional neural network in predicting anterior chamber angle after ICL implantation

Trial Locations

Locations (1)

The Second Affiliated Hospital of Nanchang University; 🇨🇳 Nanchang, Jiangxi, China