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Diagnostic Efficacy of CNN in Differentiation of Visual Field

NCT03759483 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 437

Last updated 2020-01-27

No results posted yet for this study

Summary

Glaucoma is currently the leading cause of irreversible blindness in the world. The multi-center study is designed to evaluate the efficacy of the convolutional neural network based algorithm in differentiation of glaucomatous from non-glaucomatous visual field, and to assess its utility in the real world.

Conditions

  • Diagnositic Efficacy of Deep Convolutional Neural Network in Differentiation of Glaucoma Visual Field From Non-glaucoma Visual Field

Interventions

DIAGNOSTIC_TEST

AI diagnostic algorithm

The visual fields collected would be assessed by the algorithm and ophthalmologists independently. The performance of the algorithm and the ophthalmologists would be compared, including accuracy, AUC, sensitivity and specificity.

Sponsors & Collaborators

  • Sun Yat-sen University

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2019-03-15
Primary Completion
2019-12-31
Completion
2019-12-31

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 NCT03759483 on ClinicalTrials.gov