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.

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.

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.