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

NCT06669728 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 326

Last updated 2026-04-24

No results posted yet for this study

Summary

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

Conditions

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

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.

Sponsors & Collaborators

  • Second Affiliated Hospital of Nanchang University

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
45 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2021-01-02
Primary Completion
2027-08-31
Completion
2027-08-31

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06669728 on ClinicalTrials.gov