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.
Study Type
OBSERVATIONAL
Enrollment
300
The participant only needs to take a fundus image as usual.
Zhongshan Ophthalmic Center, Sun Yat-sen University
Guangzhou, Guangdong, China
Performance of artificial intelligence system for distinguish between good image quality and poor image quality
Area under the receiver operating characteristic curves, sensitivity, specificity, positive and negative predictive values, accuracy
Time frame: 3 months
The comparison of the performance for previous artificial intelligence diagnostic system with fundus images of different image quality
Cohen's kappa coefficient, P value and other related statistic results
Time frame: 3 months
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