Study on the Diagnostic Efficacy of ICL Selection and Prediction Depth Model Based on Eye Images
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: AI diagnostic algorithm.
- Who it may be relevant to
- Registry conditions: Posterior Chamber Phakic Intraocular Lens, Vault, Deep Neural Network, Myopia. Basic parameters: 18 years — 45 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Official title
Diagnostic Efficacy of Deep Neural Network Algorithm Based on Preoperative Scheimpflug-based Anterior Segment Image for Implantable Collamer Lens Selection and Prediction
Overview
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 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.
Primary outcome measures
- AUROC 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 [Time frame: Day 7]
Secondary outcome measures (2)
- 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]
Eligibility criteria
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.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Observational model
- Other
Study locations
China · 1 center
- The Second Affiliated Hospital of Nanchang University — Nanchang
Identifiers
NCT: NCT06669728 · [2024] NO.(93)