Diagnostic Performance of Deep Learning for Angle Closure

Diagnostic Performance of Deep Convolutional Neural Networks for Angle Closure Glaucoma: an International Multicenter Study

Status

UNKNOWN

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT04242108

Enrollment

3000

Registered

2020-01-27

Start date

2019-01-15

Completion date

2022-03-31

Last updated

2021-04-08

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Angle Closure Glaucoma

Brief 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.

Interventions

DIAGNOSTIC_TESTDeep 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.

Study design

Observational model

OTHER

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

Healthy volunteers

Yes

Inclusion criteria

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).

Design outcomes

Primary

Measure	Time frame	Description
Area under receiver operating curve (AUC)	Immediately after obtaining the AS-OCT images	AUC value of the deep learning algorithm in angle width classfication and synechia detection

Secondary

Measure	Time frame	Description
Sensitivity and specificity	Immediately after obtaining the AS-OCT images	Sensitivity and specificity of the automated algorithm in angle width classfication and synechia detection

Countries

China

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026