We developed an artificial intelligence model based on a convolutional neural network (CNN) for the automatic detection of blood content in CCE images.  Training and validation datasets were constructed for the development and testing of the CNN.  The CNN detected blood with a sensitivity, specificity, and positive and negative predictive values of 99.8 %, 93.2 %, 93.8 %, and 99.8 %, respectively. 
CNN
We developed an artificial intelligence model based on a convolutional neural network (CNN) for the automatic detection of blood content in CCE images.  Training and validation datasets were constructed for the development and testing of the CNN.  The CNN detected blood with a sensitivity, specificity, and positive and negative predictive values of 99.8 %, 93.2 %, 93.8 %, and 99.8 %, respectively. 
CNN
We developed an artificial intelligence model based on a convolutional neural network (CNN) for the automatic detection of blood content in CCE images.  Training and validation datasets were constructed for the development and testing of the CNN.  The CNN detected blood with a sensitivity, specificity, and positive and negative predictive values of 99.8 %, 93.2 %, 93.8 %, and 99.8 %, respectively. 
CNN