Diagnostic Performance of Deep Learning for Angle Closure

Sun Yat-sen University3,000 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

3,000

Conditions

Angle Closure Glaucoma

Interventions

Deep learning algorithm based on AS-OCT scansDIAGNOSTIC_TEST

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.

Eligibility

Sex: ALLMin age: 18 YearsHealthy volunteers:

Medical Language ↔ Plain English

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).

Locations (1)

Zhongshan Ophthalmic Center

Guangzhou, Guangdong, China

Outcomes

Primary Outcomes

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

Secondary Outcomes

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

Data from ClinicalTrials.gov

This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.