Diagnostic Performance of Deep Learning for Angle Closure

NCT04242108 | Unknown

• 1 other identifier: 2018KYPJ074
• Study Type: observational
• Target: 3,000
• Locations: 1 country
• Sites: 1

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.

Conditions

Angle Closure Glaucoma

Outcome Measures

Primary Outcomes (1)

• Area under receiver operating curve (AUC)

  AUC value of the deep learning algorithm in angle width classfication and synechia detection

  Immediately after obtaining the AS-OCT images

Secondary Outcomes (1)

• Sensitivity and specificity

  Immediately after obtaining the AS-OCT images

Study Arms (4)

Angle closure group

Diagnostic Test: Deep learning algorithm based on AS-OCT scans

Open angle group

Diagnostic Test: Deep learning algorithm based on AS-OCT scans

Peripheral synechia (PAS) group

Diagnostic Test: Deep learning algorithm based on AS-OCT scans

Non-peripheral synechia (PAS) group

Diagnostic Test: Deep learning algorithm based on AS-OCT scans

Interventions

Deep learning algorithm based on AS-OCT scans DIAGNOSTIC_TEST

The OCT scans of study subjects would be imported into the algorithm. Automated classfication of angle width and detection of synechia would be performed by the algorithm. The diagnostic performance of the algorithm would be compared with gonioscopy records.

Angle closure group; Non-peripheral synechia (PAS) group; Open angle group; Peripheral synechia (PAS) 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

The training and primary validation datasets were collected from the databases of electronic medical and research records at Zhongshan Ophthalmic Center from September 1, 2016, to September 1, 2019. The external test dataset was obtained from the Singapore Eye Research Institute (SERI), Singapore during June 2008 to November 2019, and the Chulalongkorn University and King Chulalongkorn Memorial Hospital (KCMH, Bangkok, Thailand) from October, 2019 to April, 2020.

Sponsors & Collaborators

• Sun Yat-sen University: lead

Study Sites (1)

Zhongshan Ophthalmic Center

Guangzhou, Guangdong, 51000, China

Location

MeSH Terms

Conditions

Glaucoma, Angle-Closure

Condition Hierarchy (Ancestors)

Glaucoma; Ocular Hypertension; Eye Diseases

Study Design

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

Study Record Dates

• First Submitted: January 23, 2020
• First Posted: January 27, 2020
• Study Start: January 15, 2019
• Primary Completion: December 1, 2021
• Study Completion: March 1, 2022
• Last Updated: April 8, 2021

Record last verified: 2021-04

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, ANALYTIC CODE
• Time Frame: Part of the data would be open as public datasets after the related article is published.

Locations

CN (1)