Diagnostic Performance of Deep Learning for Angle Closure
- Conditions
- Angle Closure Glaucoma
- Interventions
- Diagnostic Test: Deep learning algorithm based on AS-OCT scans
- 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
Recruitment & Eligibility
- Status
- UNKNOWN
- Sex
- All
- Target Recruitment
- 3000
Not provided
Not provided
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description Angle closure group Deep learning algorithm based on AS-OCT scans - Open angle group Deep learning algorithm based on AS-OCT scans - Peripheral synechia (PAS) group Deep learning algorithm based on AS-OCT scans - Non-peripheral synechia (PAS) group Deep learning algorithm based on AS-OCT scans -
- 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