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Artificial intelligence to distinguish retinal vein occlusion patients using color fundus photographs



Our aim is to establish an AI model for distinguishing color fundus photographs (CFP) of RVO patients from normal individuals.


The training dataset included 2013 CFP from fellow eyes of RVO patients and 8536 age- and gender-matched normal CFP. Model performance was assessed in two independent testing datasets. We evaluated the performance of the AI model using the area under the receiver operating characteristic curve (AUC), accuracy, precision, specificity, sensitivity, and confusion matrices. We further explained the probable clinical relevance of the AI by extracting and comparing features of the retinal images.


Our model achieved an average AUC was 0.9866 (95% CI: 0.9805–0.9918), accuracy was 0.9534 (95% CI: 0.9421–0.9639), precision was 0.9123 (95% CI: 0.8784–9453), specificity was 0.9810 (95% CI: 0.9729–0.9884), and sensitivity was 0.8367 (95% CI: 0.7953–0.8756) for identifying fundus images of RVO patients in training dataset. In independent external datasets 1, the AUC of the RVO group was 0.8102 (95% CI: 0.7979–0.8226), the accuracy of 0.7752 (95% CI: 0.7633–0.7875), the precision of 0.7041 (95% CI: 0.6873–0.7211), specificity of 0.6499 (95% CI: 0.6305–0.6679) and sensitivity of 0.9124 (95% CI: 0.9004–0.9241) for RVO group. There were significant differences in retinal arteriovenous ratio, optic cup to optic disc ratio, and optic disc tilt angle (p = 0.001, p = 0.0001, and p = 0.0001, respectively) between the two groups in training dataset.


We trained an AI model to classify color fundus photographs of RVO patients with stable performance both in internal and external datasets. This may be of great importance for risk prediction in patients with retinal venous occlusion.

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Fig. 1: Workflow of our experimental design.
Fig. 2: The diagnostic efficiency of our AI model.
Fig. 3: Representative images of fundus photograph and their heat maps in training dataset and independent external testing datasets.

Data availability

The datasets analysed during the current study are available from the corresponding author on reasonable request.


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We thank the Chengdu Ikangguobin Health Examination Center for provide the color fundus photographs.


This work was supported by The Project of National Key Research and Development (No. 2018YFC1106103) to MZ and Post-Doctor Research Project, West China Hospital, Sichuan University (2021HXBH030) to XR. And Natural Science Foundation of Sichuan Province (No. 2022NSFSC1285) to XR.

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Authors and Affiliations



XR, BW, and MZ designed the study, directed the project, and interpreted the data. XR, YG, RR, YT, WF, LJ, LH, WX, XL, TW, and YC performed the experiments. GZ provided guidance for this project. XR wrote the paper, and YL, XF, GZ, and MZ contributed to editing. All authors contributed to the paper and approved the submitted version.

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Correspondence to Ming Zhang.

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Ren, X., Feng, W., Ran, R. et al. Artificial intelligence to distinguish retinal vein occlusion patients using color fundus photographs. Eye (2022).

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