Diagnostic Efficacy of CNN in Differentiation of Visual Field

NCT03759483 | Completed

• 1 other identifier: 2018KYPJ125
• Study Type: observational
• Target: 437
• Locations: 1 country
• Sites: 1

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.

Conditions

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

Keywords

Deep convolutional neural network; Visual field; Glaucoma

Outcome Measures

Primary Outcomes (1)

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

  from Jan 2019 to Jan 2020

Secondary Outcomes (1)

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

  from Jan 2019 to Jan 2020

Study Arms (2)

AI group

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

Diagnostic Test: AI diagnostic algorithm

Human group

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

Interventions

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.

Also known as: Standard diagnostic procedure

AI group

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients from clinics in different eye centers across China. Each subject must be diagnosed based on comprehensive medical tests and medical records. The leading center will read all the medical data to give out diagnosis as the gold standard.

You may qualify if:

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

You may not qualify if:

• Incomplete clinical data to support diagnosis

Sponsors & Collaborators

• Sun Yat-sen University: lead

Study Sites (1)

Zhongshan Ophthalmic Center

Guangzhou, Guangdong, 51000, China

Location

MeSH Terms

Conditions

Glaucoma

Condition Hierarchy (Ancestors)

Ocular Hypertension; Eye Diseases

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Director of Clinical Research Center，Director of Institution of Drug Clinical Trials

Study Record Dates

• First Submitted: November 26, 2018
• First Posted: November 30, 2018
• Study Start: March 15, 2019
• Primary Completion: December 31, 2019
• Study Completion: December 31, 2019
• Last Updated: January 27, 2020

Record last verified: 2020-01

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)