Diagnostic Efficacy of CNN in Differentiation of Visual Field

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

Official title:

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT03759483
• Other study ID #: 2018KYPJ125
• Status: Completed
• Start date: March 15, 2019
• Est. completion date: December 31, 2019

Study information

• Verified date: January 2020
• Source: Sun Yat-sen University
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 437
• Est. completion date: December 31, 2019
• Est. primary completion date: December 31, 2019
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

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

Related Conditions & MeSH terms

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

Intervention

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.

Locations

Country	Name	City	State
China	Zhongshan Ophthalmic Center	Guangzhou	Guangdong

Sponsors (1)

Lead Sponsor	Collaborator
Sun Yat-sen University

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	AUC value of convolutional neural network in differentiation of Glaucoma visual field from non-glaucoma visual field		from Jan 2019 to Jan 2020
Secondary	Sensitivity and specificity of convolutional neural network in detection of glaucoma visual field		from Jan 2019 to Jan 2020