RecruitingNCT06669728

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

Sponsor

Second Affiliated Hospital of Nanchang University

Enrollment

326 participants

Start Date

Jan 2, 2021

Study Type

OBSERVATIONAL

Conditions

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

Summary

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

Eligibility

Min Age: 18 YearsMax Age: 45 Years

Inclusion Criteria5

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 Criteria5

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.

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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.