Diagnostic Performance of Deep Learning for Angle Closure
Diagnostic Performance of Deep Learning for Angle Closure
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.
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).