UnknownNot Applicable

Diagnostic Performance of Deep Learning for Angle Closure

China3,000 participantsStarted 2019-01-15

Plain-language 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.

Who can participate

Age range18 Years

SexALL

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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).

What they're measuring

Area under receiver operating curve (AUC)

Timeframe: Immediately after obtaining the AS-OCT images

Trial details

NCT IDNCT04242108

SponsorSun Yat-sen University

Sponsor typeOTHER

Study typeOBSERVATIONAL

Primary completion2021-12

View on ClinicalTrials.gov More Angle Closure Glaucoma trials