Real-world Diagnostic Effectiveness of Artificial Intelligence Algorithm

Status Recruiting

Clinical Trial 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.

Clinical Trial 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 Design

Related Conditions & MeSH terms

Clinical Trial Details

NCT number	NCT03911323
Study type	Observational
Source	Shenzhen Second People's Hospital
Contact	Jiying Hu, PhD
Phone	+86-1808-6481-821
Email	18086481821@163.com
Status	Recruiting
Phase
Start date	October 1, 2018
Completion date	October 1, 2020

View Details

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Clinical trials are conducted in a series of steps, called phases - each phase is designed to answer a separate research question.

Phase 1: Researchers test a new drug or treatment in a small group of people for the first time to evaluate its safety, determine a safe dosage range, and identify side effects.
Phase 2: The drug or treatment is given to a larger group of people to see if it is effective and to further evaluate its safety.
Phase 3: The drug or treatment is given to large groups of people to confirm its effectiveness, monitor side effects, compare it to commonly used treatments, and collect information that will allow the drug or treatment to be used safely.
Phase 4: Studies are done after the drug or treatment has been marketed to gather information on the drug's effect in various populations and any side effects associated with long-term use.

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

Clinical Trial Summary

Clinical Trial Description

Study Design

Related Conditions & MeSH terms