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NCT Number: NCT06669728

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

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

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Key information

Age range

18 year–45 year

Sex eligibility

All sexes

Study type

Observational

Primary location

About this 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.

Who can participate

Healthy volunteers accepted: Yes

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

Inclusion criteria

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

Exclusion criteria

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

Treatment and study plan

AI diagnostic algorithm

Diagnostic 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.

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

  2. AUROC of convolutional neural network in predicting anterior chamber angle after ICL implantation

    Time frame: Day 7

    The area under the receiver operating characteristic of convolutional neural network in predicting anterior chamber angle after ICL implantation

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

  2. Sensitivity and specificity of convolutional neural network in predicting anterior chamber angle after ICL implantation

    Time frame: Day 7

    Sensitivity and specificity of convolutional neural network in predicting anterior chamber angle after ICL implantation

Study contacts

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

Fu Gui docter

CONTACT

[email protected]

+8613879101919

Jian Xiong doctor

CONTACT

[email protected]

+8618170906556

Sponsors and collaborators

Lead sponsor

Second Affiliated Hospital of Nanchang University

Other

Registry information

Official study title

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

Important dates

Study start
2021
Primary completion
2027
Study completion
2027
First posted
Nov 1, 2024
Registry last updated
Apr 24, 2026

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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