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Purpose To evaluate the effectiveness of automated fundus screening software in detecting eye diseases by comparing the reported results against those given by human experts. Results There were 1585 subjects who completed the procedure and yielded qualified images. The prevalence of referable diabetic retinopathy (RDR), glaucoma suspect (GCS), and referable macular diseases (RMD) were 20.4%, 23.2%, and 49.0%, respectively. The overall sensitivity values for RDR, GCS, and RMD diagnosis are 0.948 (95% confidence interval [CI], 0.918–0.967), 0.891 (95% CI, 0.855–0.919), and 0.901 (95% CI–0.878, 0.920), respectively. The overall specificity values for RDR, GCS, and RMD diagnosis are 0.954 (95% CI, 0.915–0.965), 0.993 (95% CI–0.986, 0.996), and 0.955 (95% CI–0.939, 0.968), respectively. Methods We prospectively enrolled 1743 subjects at seven hospitals throughout China. At each hospital, an operator records the subjects' information, takes fundus images, and submits the images to the Image Reading Center of Zhongshan Ophthalmic Center, Sun Yat-Sen University (IRC). The IRC grades the images according to the study protocol. Meanwhile, these images will also be automatically screened by the artificial intelligence algorithm. Then, the analysis results of automated screening algorithm are compared against the grading results of IRC. The end point goals are lower bounds of 95% CI of sensitivity values that are greater than 0.85 for all three target diseases, and lower bounds of 95% CI of specificity values that are greater than 0.90 for RDR and 0.85 for GCS and RMD. Conclusions Automated fundus screening software demonstrated a high sensitivity and specificity in detecting RDR, GCS, and RMD from color fundus imaged captured using various cameras. Translational Relevance These findings suggest that automated software can improve the screening effectiveness for eye diseases, especially in a primary care context, where experienced ophthalmologists are scarce.

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There are extensive researches focusing on automated diabetic retinopathy (DR) detection from fundus images. However, the accuracy drop is observed when applying these models in real-world DR screening, where the fundus camera brands are different from the ones used to capture the training images. How can we train a classification model on labeled fundus images acquired from only one camera brand, yet still achieves good performance on images taken by other brands of cameras? In this paper, we quantitatively verify the impact of fundus camera brands related domain shift on the performance of DR classification models, from an experimental perspective. Further, we propose camera-oriented residual-CycleGAN to mitigate the camera brand difference by domain adaptation and achieve increased classification performance on target camera images. Extensive ablation experiments on both the EyePACS dataset and a private dataset show that the camera brand difference can significantly impact the classification performance and prove that our proposed method can effectively improve the model performance on the target domain. We have inferred and labeled the camera brand for each image in the EyePACS dataset and will publicize the camera brand labels for further research on domain adaptation.