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

Second Affiliated Hospital of Nanchang University326 enrolled

Overview

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

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

326

Conditions

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

Interventions

AI diagnostic algorithmDIAGNOSTIC_TEST

The ICL procedures collected would be assessed by the algorithm. The performance of the algorithm would be assessed, including accuracy, AUC, sensitivity and specificity.

Eligibility

Sex: ALLMin age: 18 YearsMax age: 45 YearsHealthy volunteers:

Medical Language ↔ Plain English

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.

Locations (1)

The Second Affiliated Hospital of Nanchang University

Nanchang, Jiangxi, China

RECRUITING

Outcomes

Primary Outcomes

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

Time frame: 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

Time frame: Day 7

Secondary Outcomes

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 anterior chamber angle after ICL implantation

Time frame: Day 7

Central Contacts

Jian Xiong doctor

CONTACT

+8618170906556 894040417@qq.com

Fu Gui docter

CONTACT

+8613879101919 564436578@qq.com

Data from ClinicalTrials.gov

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