Diagnostic Performance of Deep Learning for Angle Closure

Angle Closure Glaucoma Clinical Trial

Official title:

Diagnostic Performance of Deep Convolutional Neural Networks for Angle Closure Glaucoma: an International Multicenter Study

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT04242108
• Other study ID #: 2018KYPJ074
• Status: Active, not recruiting
• Start date: January 15, 2019
• Est. completion date: March 2022

Study information

• Verified date: April 2021
• Source: Sun Yat-sen University
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 3000
• Est. completion date: March 2022
• Est. primary completion date: December 2021
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

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 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).

Related Conditions & MeSH terms

• Angle Closure Glaucoma
• Glaucoma
• Glaucoma, Angle-Closure

Intervention

Diagnostic Test:
• 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.

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