AI technology to help reduce the risk of vision loss, stroke
Our aim is to establish an AI model for distinguishing color fundus photographs 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 , 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 , accuracy was 0.9534 , precision was 0.9123 , specificity was 0.9810 , and sensitivity was 0.8367 for identifying fundus images of RVO patients in training dataset. In independent external datasets 1, the AUC of the RVO group was 0.8102 , the accuracy of 0.7752 , the precision of 0.7041 , specificity of 0.
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