Diagnostic Performance of Deep Learning for Angle Closure

• Conditions: Angle Closure Glaucoma

• Registration Number: NCT04242108
• Lead Sponsor: Sun Yat-sen University

Brief Summary

Primary angle closure diseases (PACD) are commonly seen in Asia. In clinical practice, gonioscopy is the gold standard for angle width classification in PACD patietns. However, gonioscopy is a contact examination and needs a long learning curve. Anterior segment optical coherence tomography (AS-OCT) is a non-contact test which can obtain three dimensional images of the anterior segment within seconds. Therefore, the investigators designed the study to verify if AS-OCT based deep learning algorithm is able to detect the PACD subjects diagnosed by gonioscopy.

Detailed Description

Not available

Study Timeline

• Study Start: 2019-01-15
• Study First Submitted: 2020-01-23
• Study First Submitted QC: 2020-01-23
• Study First Posted: 2020-01-27
• Status Verified: 2021-04
• Last Update Submitted: 2021-04-03
• Last Update Posted: 2021-04-08
• Primary Completion: 2021-12
• Study Completion: 2022-03

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 3000

Inclusion Criteria

Not provided

Exclusion Criteria

Not provided

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Area under receiver operating curve (AUC)	Immediately after obtaining the AS-OCT images	AUC value of the deep learning algorithm in angle width classfication and synechia detection

Secondary Outcome Measures

Name	Time	Method
Sensitivity and specificity	Immediately after obtaining the AS-OCT images	Sensitivity and specificity of the automated algorithm in angle width classfication and synechia detection

Trial Locations

Locations (1)

Zhongshan Ophthalmic Center; 🇨🇳 Guangzhou, Guangdong, China