Evaluating a deep learning model for selecting implantable collamer lenses using eye images

Is this trial a fit for you?

A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.

What this trial studies

This observational study aims to assess the diagnostic efficacy of a deep learning network model in selecting and predicting the appropriate implantable collamer lens (ICL) for myopia correction. By utilizing eye images, the study seeks to optimize the selection process for ICL design, which is often complicated by varying biological parameters. The research will be conducted across multiple centers and aims to enhance the accuracy, safety, and predictability of ICL implantation, ultimately assisting physicians in their decision-making process.

Why it matters

Who can join: the full rules

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.

Where it's running

• The Second Affiliated Hospital of Nanchang University
  Nanchang, Jiangxi, China Recruiting

Study contacts

• Study coordinator: Jian Xiong doctor
• Email: 894040417@qq.com
• Phone: +8618170906556

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How to take part

1. Review the eligibility criteria above with your treating physician.
2. Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
3. Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.