Real-world of AI in Diagnosing Retinal Diseases

Artificial Intelligence Clinical Trial

Official title:

Real-world Application of Using Artificial Intelligence in Diagnosing Retinal Diseases

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05981950
• Other study ID #: Real-world RAIDS
• Status: Recruiting
• Start date: August 1, 2023
• Est. completion date: August 1, 2029

Study information

• Verified date: August 2023
• Source: Beijing Tongren Hospital
• Contact: Wenbin Wei, MD
• Phone: 58269516
• Email: weiwenbintr[@]163.cim
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 100000
• Est. completion date: August 1, 2029
• Est. primary completion date: August 1, 2028
• Gender: All
• Age group: 1 Year to 100 Years

Eligibility

Inclusion Criteria:

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

• complete identification information

Exclusion Criteria:

• insufficient information for diagnosis

Related Conditions & MeSH terms

• Artificial Intelligence
• Retinal Diseases

Intervention

Diagnostic Test:
• artificial intelligence algorithm
Retinal diseases diagnosed by artificial intelligence algorithm

Locations

Country	Name	City	State
China	Wen-Bin Wei	Beijing	Beijing

Sponsors (1)

Lead Sponsor	Collaborator
Beijing Tongren Hospital

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Area under curve	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.	1 month
Primary	Sensitivity and specificity	We used sensitivity and specificity to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.	1 month
Primary	Positive predictive value, negative predictive value	We used positive predictive value and negative predictive value to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.	1 month
Primary	F1 score	We used F1 score to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.	1 month