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

Diagnostic Efficacy of Deep Neural Network Algorithm Based on Preoperative Scheimpflug-based Anterior Segment Image for Implantable Collamer Lens Selection and Prediction

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

Study Overview

• Status: Recruiting
• Conditions: Myopia; Posterior Chamber Phakic Intraocular Lens; Vault; Deep Neural Network; Anterior Chamber Angle
• Intervention / Treatment: Diagnostic test: AI diagnostic algorithm

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 Type: Observational
• Enrollment (Estimated): 326

Contacts and Locations

• Study Contact: Name: Jian Xiong doctor; Phone Number: +8618170906556; Email: 894040417@qq.com
• Study Contact Backup: Name: Fu Gui docter; Phone Number: +8613879101919; Email: 564436578@qq.com

Study Locations

• China
  • Jiangxi
    • Nanchang, Jiangxi, China, 330000
      • Recruiting
      • The Second Affiliated Hospital of Nanchang University
      • Contact: Jian Xiong doctor; Phone Number: +8618170906556; Email: 894040417@qq.com
      • Contact: Fu Gui doctor; Phone Number: +8613879101919; Email: 564436578@qq.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

Patients from clinics in different eye centers across China. Each subject must with complete surgical video recording and medical records.

Description

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 Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Eyes with ICL surgeries; Eyes with SMILE surgeries which were performed by surgeons with experiences.	Diagnostic test: AI diagnostic algorithm; The ICL procedures collected would be assessed by the algorithm. The performance of the algorithm would be assessed, including accuracy, AUC, sensitivity and specificity.

What is the study measuring?

Primary Outcome Measures

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

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Sensitivity and specificity of convolutional neural network in predicting Vault after ICL implantation	Sensitivity and specificity of convolutional neural network in predicting Vault after ICL implantation	Day 7
Sensitivity and specificity of convolutional neural network in predicting anterior chamber angle after ICL implantation	Sensitivity and specificity of convolutional neural network in predicting anterior chamber angle after ICL implantation	Day 7

Collaborators and Investigators

• Sponsor: Second Affiliated Hospital of Nanchang University
• Collaborators: Nanchang Bright Eye Hospital

Study record dates

Study Major Dates

• Study Start (Actual): January 2, 2021
• Primary Completion (Estimated): August 31, 2027
• Study Completion (Estimated): August 31, 2027

Study Registration Dates

• First Submitted: October 30, 2024
• First Submitted That Met QC Criteria: October 30, 2024
• First Posted (Actual): November 1, 2024

Study Record Updates

• Last Update Posted (Actual): April 24, 2026
• Last Update Submitted That Met QC Criteria: April 21, 2026
• Last Verified: January 1, 2026

More Information

Terms related to this study

• Keywords: Deep Neural Network; Posterior Chamber Phakic Intraocular Lens; Vault
• Additional Relevant MeSH Terms: Eye Diseases; Refractive Errors; Myopia
• Other Study ID Numbers: [2024] NO.(93)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No