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OpenTrials
Completed

NCT Number: NCT03759483

Diagnostic Efficacy of CNN in Differentiation of Visual Field

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.

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Key information

Conditions

Diagnositic Efficacy of Deep Convolutional Neural Network in Differentiation of Glaucoma Visual Field From Non-glaucoma Visual Field Eye Diseases Glaucoma Ocular Hypertension

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Zhongshan Ophthalmic Center

Guangzhou, Guangdong, 51000, China

About this study

Glaucoma is the world's leading cause of irreversible blind, characterized by progressive retinal nerve fiber layer thinning and visual field defects. Visual field test is one of the gold standards for diagnosis and evaluation of progression of glaucoma. However, there is no universally accepted standard for the interpretation of visual field results, which is subjective and requires a large amount of experience. At present, artificial intelligence has achieved the accuracy comparable to human physicians in the interpretation of medical imaging of many different diseases. Previously, we have trained a deep convolutional neural network to read the visual field reports, which has even higher diagnostic efficacy than ophthalmologists. The current 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, compare its performance with ophthalmologists and to assess its utility in the real world.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Age≥18;
  • Informed consent obtained;
  • Diagnosed with specific ocular diseases;
  • Able to perform visual field test

Exclusion criteria

Incomplete clinical data to support diagnosis

Treatment and study plan

AI diagnostic algorithm

Diagnostic Test

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.

Other names: Standard diagnostic procedure

Primary outcomes

  1. AUC value of convolutional neural network in differentiation of Glaucoma visual field from non-glaucoma visual field

    Time frame: from Jan 2019 to Jan 2020

Secondary outcomes

  1. Sensitivity and specificity of convolutional neural network in detection of glaucoma visual field

    Time frame: from Jan 2019 to Jan 2020

Sponsors and collaborators

Lead sponsor

Sun Yat-sen University

Other

Registry information

Official study title

Diagnostic Efficacy of Convolutional Neural Network Based Algorithm in Differentiation of Glaucomatous Visual Field From Non-glaucomatous Visual Field

Important dates

Study start
2019
Primary completion
2019
Study completion
2019
First posted
Nov 30, 2018
Registry last updated
Jan 27, 2020

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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