Artificial Intelligence, Retinal Diseases
Conditions
Brief summary
Fundus images are widely used in ophthalmology for the detection of diabetic retinopathy, glaucoma and other diseases. In real-world practice, the quality of fundus images can be unacceptable, which can undermine diagnostic accuracy and efficiency. Here, the researchers established and validated an artificial intelligence system to achieve automatic quality assessment of fundus images upon capture. This system can also provide guidance to photographers according to the reasons for low quality.
Interventions
DEVICETaking a fundus image
The participant only needs to take a fundus image as usual.
Sponsors
Sun Yat-sen University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
Yes
Inclusion criteria
* Patients should be aware of the contents and signed for the informed consent.
Exclusion criteria
* 1\. Patients who cannot cooperate with a photographer such as some paralytics, the patients with dementia and severe psychopaths. * 2\. Patients who do not agree to sign informed consent.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Performance of artificial intelligence system for distinguish between good image quality and poor image quality | 3 months | Area under the receiver operating characteristic curves, sensitivity, specificity, positive and negative predictive values, accuracy |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| The comparison of the performance for previous artificial intelligence diagnostic system with fundus images of different image quality | 3 months | Cohen's kappa coefficient, P value and other related statistic results |
Countries
China
Outcome results
None listed