NCT04242108

Diagnostic Performance of Deep Learning for Angle Closure

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

• Status: Unknown
• Phase: —
• Study type: Observational
• Enrollment: 3,000 (estimated)
• Sponsor: Sun Yat-sen University · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Accepted

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

Interventions

Type	Name	Description
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.

Timeline

• Start date: 2019-01-15
• Primary completion: 2021-12-01
• Completion: 2022-03-01
• First posted: 2020-01-27
• Last updated: 2021-04-08

Locations

1 site across 1 country: China