Diabetic Retinopathy (DR)
Conditions
Brief summary
This study is being done to evaluate the performance of a software that uses artificial intelligence to analyze photographs of the retina to help detect diabetic retinopathy. The study will also assess the safety of the software in combination with a fundus camera already available on the market. This software analyzes retinal photographs to detect more than mild diabetic retinopathy in adults with diabetes. The results will be compared to expert human evaluations.
Interventions
DEVICEautomatic analysis of fundus images performed by the software tool based on artificial intelligence
the duration of the intervention is the analysis performed by the software tool based on fundus images acquired
Sponsors
Centervue SpA
Study design
Observational model
CASE_ONLY
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
22 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* 22 years old or older * diabetes or diabetic retinopathy * understand the study information and able to sign a consent form
Exclusion criteria
* cannot tolerate eye imaging tests * laser treatment or injections * eye surgery, except for simple cataract surgery * currently involved in another study * pregnant * cannot or do not want to have your eyes dilated * photodynamic therapy within the last 90 days
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Number of subjects whose results provided by the automatic AI-based tool match the reading center grading for the identification of referable diabetic eye disease (more than mild DR). | 1-day visit |
Secondary
| Measure | Time frame |
|---|---|
| percentage of eyes for which the AI-based automatic tool produced a result | 1-day visit |
Contacts
CONTACTRui
CONTACTRumiato
Outcome results
None listed