Diagnostic Efficacy of CNN in Differentiation of Visual Field

NCT ID: NCT03759483

Last Updated: 2020-01-27

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 437 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2019-03-15
• Study Completion Date: 2019-12-31

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.

Conditions

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

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: OTHER

Study Groups

AI group

The visual field reports in this group will be evaluated by the convolutional neural network.

AI diagnostic algorithm

Intervention Type: 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.

Human group

The visual field reports in this group will be evaluated by 3 ophthalmologists independently.

No interventions assigned to this group

Interventions

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.

Intervention Type: DIAGNOSTIC_TEST

Other Intervention Names

Standard diagnostic procedure

Eligibility Criteria

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

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Sun Yat-sen University

OTHER

Sponsor Role: lead

Responsible Party

Xiulan Zhang

Director of Clinical Research Center，Director of Institution of Drug Clinical Trials

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Zhongshan Ophthalmic Center

Guangzhou, Guangdong, China

Countries

China

Other Identifiers

2018KYPJ125

Identifier Type: -

Identifier Source: org_study_id