Real-world Diagnostic Effectiveness of Artificial Intelligence Algorithm in Diabetic Retinopathy Screening

• Conditions: Diabetic Retinopathy

• Registration Number: NCT03911323
• Lead Sponsor: Shenzhen Second People's Hospital

Brief Summary

Recently, artificial intelligence algorithm has made great progress in the prediction of diabetic retinopathy based on fundus images，showing very high sensitivity and specificity. However，the real-world diagnosis effectiveness of deep learning model is still unclear.

This study is designed to evaluate the clinical efficacy of such an algorithm in detecting referable diabetic retinopathy.

Detailed Description

This prospective clinical study is designed to evaluate the real-world diagnostic performance of an AI model in detecting referable diabetic retinopathy (RDR, defined as more than mild NPDR), by evaluating its sensitivity and specificity compared to the clinical reference standard- seven-field stereoscopic photography.

The subjects enrolled in this study are patients with T1DM or T2DM. Qualified color fundus images and seven-field stereoscopic photography images of each eyes of the subject are taken. The fundus images are graded for RDR by the algorithm under test, and seven-field stereoscopic photography images of the same eye are graded by ophthalmologist, which serving as the gold standard to compare the algorithm performance against.

The trial plans to enroll 1000 subjects. With a 95% confidence interval, the sensitivity is expected to be at least 87% whereas the specificity at 89% or above.

The quality of fundus images are assessed according to the National DR Screening Imaging and Grading Guideline published by Chinese Ophthalmological Society and Chinese Medical Doctor Association in 2017.

The grading of RDR is based on the National DR Clinical Diagnosis and Treatment Guideline published by Chinese Ophthalmological Society in 2014.

A brief overview of the clinical protocol is as follows:

Candidate recruiting phase: recruiting qualified participants; Clinical phase: imaging and diagnosing by AI and ophthalmologist ; Statistical analysis phase: comparing two outputs; Closing phase: final report and archiving

Study Timeline

• Study Start: 2018-10-01
• Status Verified: 2019-04
• Study First Submitted: 2019-04-09
• Study First Submitted QC: 2019-04-09
• Last Update Submitted: 2019-04-09
• Study First Posted: 2019-04-11
• Last Update Posted: 2019-04-11
• Primary Completion: 2020-10-01
• Study Completion: 2020-10-01

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 1000

Inclusion Criteria

1. Subject must understand the study, participate voluntarily, and has signed informed consent
2. Age 18 or older, no limitations on gender identity
3. Patients with type 1 or type 2 diabetes.

Exclusion Criteria

1. Subjects diagnosed with eye diseases other than diabetic retinopathy
2. Subjects diagnosed with macular edema, severe non-proliferative retinopathy, proliferative retinopathy, radioactive retinopathy or retinal vein obstruction.
3. Pregnant woman, subjects with mydriatic allergy, unclear refractive medium, family history of glaucoma, or diagnosed as narrow angle
4. Subjects with a history of laser therapy, retinal surgery or anti-vascular endothelial growth factor injection
5. Subjects currently participating in another ophthalmic research, receiving ophthalmic research products.
6. Subject who is photo-sensitivity or taking medication that causes photosensitivity
7. Subjects received photodynamic therapy recently

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Sensitivity and specificity	No more than 1 day for each subject	To evaluate the sensitivity and specificity of the model in detecting referable DR (more than mild NPDR)

Trial Locations

Locations (1)

Shenzhen second peoples's hospital; 🇨🇳 Shenzhen, Guangdong, China