Angle Closure Glaucoma Clinical Trial
Official title:
Diagnostic Performance of Deep Convolutional Neural Networks for Angle Closure Glaucoma: an International Multicenter Study
Verified date | April 2021 |
Source | Sun Yat-sen University |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Observational |
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.
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). |
Country | Name | City | State |
---|---|---|---|
China | Zhongshan Ophthalmic Center | Guangzhou | Guangdong |
Lead Sponsor | Collaborator |
---|---|
Sun Yat-sen University |
China,
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 |
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