Diagnostic Performance of Deep Learning for Angle Closure

Study Overview
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Brief Summary
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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.

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

Conditions
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Angle Closure Glaucoma

Intervention / Treatment
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* Diagnostic Test: Deep learning algorithm based on AS-OCT scans


Participation Criteria
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Eligibility Criteria
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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).

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Angle closure group<br> | Diagnostic Test: Deep learning algorithm based on AS-OCT scans<br>* 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.<br>|
| Open angle group<br> | Diagnostic Test: Deep learning algorithm based on AS-OCT scans<br>* 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.<br>|
| Peripheral synechia (PAS) group<br> | Diagnostic Test: Deep learning algorithm based on AS-OCT scans<br>* 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.<br>|
| Non-peripheral synechia (PAS) group<br> | Diagnostic Test: Deep learning algorithm based on AS-OCT scans<br>* 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.<br>|

What is the study measuring?
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Primary Outcome Measures

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

Secondary Outcome Measures

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