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.
Study Type
OBSERVATIONAL
Enrollment
300,000
Training dataset was used to train the deep learning model, which was validated and tested by other two datasets.
Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity
Guangzhou, Guangdong, China
RECRUITINGArea under the receiver operating characteristic curve of the deep learning system
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.
Time frame: baseline
Sensitivity of the deep learning system
The investigators will calculate the sensitivity of deep learning system and compare this index between deep learning system and human doctors.
Time frame: baseline
Specificity of the deep learning system
The investigators will calculate the specificity of deep learning system and compare this index between deep learning system and human doctors.
Time frame: baseline
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