Ophthalmological Disorder
Conditions
Brief summary
Retinal images can reflect both fundus and systemic conditions (diabetes and cardiovascular disease) and firstly to be used for medical artificial intelligence (AI) algorithm training due to its advantages of clinical significance and easy to obtain. Here, the investigators developed a single network model that can mine the characteristics among multiple fundus diseases, which was trained by plenty of fundus images with one or several disease labels (if they have) in each of them. The model performance was compared with those of both native and international ophthalmologists. The model was further tested by datasets with different camera types and validated by three external datasets prospectively collected from the clinical sites where the model would be applied.
Interventions
OTHERdiagnostic
Training dataset was used to train the deep learning model, which was validated and tested by other two datasets.
Sponsors
Sun Yat-sen University
Study design
Observational model
OTHER
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Healthy volunteers
Yes
Inclusion criteria
* The quality of fundus images should clinical acceptable. More than 80% of the fundus image area including four main regions (optic disk, macular, upper and lower retinal vessel archs) are easy to read and discriminate.
Exclusion criteria
* Images with light leakage (\>30% of area), spots from lens flares or stains, and overexposure were excluded from further analysis.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Area under the receiver operating characteristic curve of the deep learning system | baseline | The investigators will calculate the area under the receiver operating characteristic curve of deep learning system and compare this index between deep learning system and human doctors. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity of the deep learning system | baseline | The investigators will calculate the sensitivity of deep learning system and compare this index between deep learning system and human doctors. |
| Specificity of the deep learning system | baseline | The investigators will calculate the specificity of deep learning system and compare this index between deep learning system and human doctors. |
Countries
China
Contacts
Primary ContactHaotian Lin, PhD
Outcome results
None listed