After the location of AOV is detected, our next goal is to classify the endoscopic images of the duodenum according to the cannulation difficulty in ERCP. Depending on how the difficulty labels were grouped, we conducted the prediction in the following two ways: binary classification and four-class classification. First, we divided all the cases into two groups, namely, “easy case” or “difficult case” group. The “difficult case” group included the cases that had the cannulation time of over 5 min, cases requiring additional cannulation techniques, and failure of selective cannulation, except easy cases, as stated earlier. Furthermore, the groups were subdivided into four-class as follows: easy class, class whose cannulation time was over 5 min, class requiring additional cannulation techniques, and failure class.

Similar to the AOV detection task, CNN-based classification models were used and transfer learning was adopted. Specifically, modified versions of VGG19 with batch normalization20, ResNet5022, and DenseNet16123 were used. VGG19 is a VGGNet architecture with 19 layers, while batch normalization is a technique that keeps the distribution of activation values in a network constant. ResNet is a CNN model that allows residual mappings by adopting skip connections between the layers and effectively alleviates the gradient vanishing problem. DenseNet uses skip connections “densely” to maximize the advantage of skip connections. A single three-channel endoscopic image is used as an input to the model. While training, data were augmented every iteration by applying various transformations to endoscopic images, e.g., flipping, shearing, and rotating. We also used early stopping to avoid overfitting. All the networks in this study were implemented with the deep learning framework called PyTorch24.

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