An observational study in Angle Closure Glaucoma, sponsored by Sun Yat-sen University. Status unknown at 1 site in China. Open to participants aged 18 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2021-04-08.
Sponsored by Sun Yat-sen University · 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.
91 studies on the registry are indexed under Glaucoma, Angle-Closure; 17 are open to participants now.
This study's planned enrollment of 3,000 is above the median of 225 across 32 observational studies indexed under Glaucoma, Angle-Closure.
Browse Glaucoma, Angle-Closure studies →Sun Yat-sen University is the lead sponsor of 1,690 studies on the registry; 619 are open to participants now.
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The training and primary validation datasets were collected from the databases of electronic medical and research records at Zhongshan Ophthalmic Center from September 1, 2016, to September 1, 2019. The external test dataset was obtained from the Singapore Eye Research Institute (SERI), Singapore during June 2008 to November 2019, and the Chulalongkorn University and King Chulalongkorn Memorial Hospital (KCMH, Bangkok, Thailand) from October, 2019 to April, 2020.
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).
Diagnostic Test: Deep learning algorithm based on AS-OCT scans
Diagnostic Test: Deep learning algorithm based on AS-OCT scans
Diagnostic Test: Deep learning algorithm based on AS-OCT scans
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.
Area under receiver operating curve (AUC)
AUC value of the deep learning algorithm in angle width classfication and synechia detection
Time frame: Immediately after obtaining the AS-OCT images
Sensitivity and specificity
Sensitivity and specificity of the automated algorithm in angle width classfication and synechia detection
Time frame: Immediately after obtaining the AS-OCT images
Plan to share: Yes — The imaging data of study subjects would be available to other researchers upon reasonable request. Part of the data would be open as public datasets after the related article is published.
Supporting information: Study protocol, Analytic code
Time frame: Part of the data would be open as public datasets after the related article is published.
No publications or documents are linked to this record.
This study is status unknown, as verified in Apr 2021. You cannot join it, but the record below documents what was studied.
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