Diagnostic Performance of Deep Learning for Angle Closure

NCT ID: NCT04242108

Last Updated: 2021-04-08

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 3000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2019-01-15
• Study Completion Date: 2022-03-31

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

Study Design

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

Study Groups

Angle closure group

Deep learning algorithm based on AS-OCT scans

Intervention Type: 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.

Open angle group

Deep learning algorithm based on AS-OCT scans

Intervention Type: 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.

Peripheral synechia (PAS) group

Deep learning algorithm based on AS-OCT scans

Intervention Type: 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.

Non-peripheral synechia (PAS) group

Deep learning algorithm based on AS-OCT scans

Intervention Type: 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.

Interventions

Deep learning algorithm based on AS-OCT scans

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.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

The inclusion criteria in the study were as follows: (1) All participants must be ≥ 18 years old; (2) Study subjects had a previous diagnosis of the ACA status (narrow or open, PAS or non-PAS) based on gonioscopy, SS-OCT scans and medical history records.

Exclusion Criteria

Exclusion criteria of the data include: (1) poor compliance in receiving gonioscopy examination; (2) unclear AS-OCT scans due to blinking or out of focus; (3) recent use of miotics within a month; 4) secondary angle closure sue to subluxation or dislocation, uveitis, neovascular glaucoma, et al.; 5) history of ocular surgery or laser iridotomy; 6) patients who previously had an episode of primary angle closure (which was obtained on history by asking the patients).

• 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

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Zhongshan Ophthalmic Center

Guangzhou, Guangdong, China

Countries

China

Other Identifiers

2018KYPJ074

Identifier Type: -

Identifier Source: org_study_id