Diagnostic Efficacy of CNN in Differentiation of Visual Field

Completed

• 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

• Registration Number: NCT03759483
• Lead Sponsor: Sun Yat-sen University

Brief 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.

Detailed Description

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.

Study Timeline

• Study First Submitted: 2018-11-26
• Study First Submitted QC: 2018-11-28
• Study First Posted: 2018-11-30
• Study Start: 2019-03-15
• Primary Completion: 2019-12-31
• Study Completion: 2019-12-31
• Status Verified: 2020-01
• Last Update Submitted: 2020-01-23
• Last Update Posted: 2020-01-27

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 437

Inclusion Criteria

1. Age≥18;
2. Informed consent obtained;
3. Diagnosed with specific ocular diseases;
4. Able to perform visual field test

Exclusion Criteria

Incomplete clinical data to support diagnosis

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
AI group	AI diagnostic algorithm	The visual field reports in this group will be evaluated by the convolutional neural network.

Primary Outcome Measures

Name	Time	Method
AUC value of convolutional neural network in differentiation of Glaucoma visual field from non-glaucoma visual field	from Jan 2019 to Jan 2020

Secondary Outcome Measures

Name	Time	Method
Sensitivity and specificity of convolutional neural network in detection of glaucoma visual field	from Jan 2019 to Jan 2020

Trial Locations

Locations (1)

Zhongshan Ophthalmic Center; 🇨🇳 Guangzhou, Guangdong, China