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

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06669728

Enrollment

326

Registered

2024-11-01

Start date

2021-01-02

Completion date

2027-08-31

Last updated

2026-04-24

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Anterior Chamber Angle, Deep Neural Network, Myopia, Posterior Chamber Phakic Intraocular Lens, Vault

Keywords

Posterior Chamber Phakic Intraocular Lens, Vault, Deep Neural Network

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.

Interventions

DIAGNOSTIC_TESTAI 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.

Study design

Observational model

OTHER

Time perspective

CROSS_SECTIONAL

Eligibility

Sex/Gender

ALL

Age

18 Years to 45 Years

Healthy volunteers

Yes

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.

Design outcomes

Primary

Measure	Time frame	Description
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

Measure	Time frame	Description
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

Countries

China

Contacts

CONTACTJian Xiong doctor

894040417@qq.com+8618170906556

CONTACTFu Gui docter

564436578@qq.com+8613879101919

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Apr 25, 2026