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Diagnostic Performance of Deep Learning for Angle Closure

NCT04242108 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 3000

Last updated 2021-04-08

No results posted yet for this study

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.

Conditions

  • Angle Closure Glaucoma

Interventions

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.

Sponsors & Collaborators

  • Sun Yat-sen University

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2019-01-15
Primary Completion
2021-12-31
Completion
2022-03-31

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04242108 on ClinicalTrials.gov