Real-world of AI in Diagnosing Retinal Diseases

Recruiting

• Conditions: Retinal Diseases; Artificial Intelligence
• Interventions: Diagnostic Test: artificial intelligence algorithm

• Registration Number: NCT05981950
• Lead Sponsor: Beijing Tongren Hospital

Brief Summary

The objective of this study is to apply an artificial intelligence algorithm to diagnose multi-retinal diseases in real-world settings. The effectiveness and accuracy of this algorithm are evaluated by sensitivity, specificity, positive predictive value, negative predictive value, and area under curve.

Detailed Description

The objective of this study is to apply an artificial intelligence algorithm to diagnose referral diabetes retinopathy, referral age-related macular degeneration, referral possible glaucoma, pathological myopia, retinal vein occlusion, macular hole, macular epiretinal membrane, hypertensive retinopathy, myelinated fibers, retinitis pigmentosa and other retinal lesions from fundus photography. tic 45-degree fundus cameras, trained operators took binocular fundus photography on participants. Operators were then asked to identify gradable images and unload for algorithm diagnosis. The effectiveness and accuracy of this algorithm are evaluated by sensitivity, specificity, positive predictive value, negative predictive value, area under curve, and F1 score.

Study Timeline

• Status Verified: 2023-08
• Study Start: 2023-08-01
• Study First Submitted: 2023-08-01
• Study First Submitted QC: 2023-08-01
• Last Update Submitted: 2023-08-01
• Study First Posted: 2023-08-08
• Last Update Posted: 2023-08-08
• Primary Completion: 2028-08-01
• Study Completion: 2029-08-01

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 100000

Inclusion Criteria

• fundus photography around 45° field which covers optic disc and macula
• complete identification information

Exclusion Criteria

• insufficient information for diagnosis

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Retinal diseases diagnosed by artificial intelligence algorithm	artificial intelligence algorithm	An artificial intelligence algorithm was applied to diagnose referral diabetes retinopathy, referral age-related macular degeneration, referral possible glaucoma, pathological myopia, retinal vein occlusion, macular hole, macular epiretinal membrane, hypertensive retinopathy, myelinated fibers, retinitis pigmentosa and other retinal lesions from fundus photography.

Primary Outcome Measures

Name	Time	Method
Area under curve	1 month	We used the receiver operating characteristic (ROC) curve and area under curve to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.
F1 score	1 month	We used F1 score to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.
Sensitivity and specificity	1 month	We used sensitivity and specificity to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.
Positive predictive value, negative predictive value	1 month	We used positive predictive value and negative predictive value to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.

Trial Locations

Locations (1)

Wen-Bin Wei; 🇨🇳 Beijing, Beijing, China