Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease

Table 1 Recommendations for reporting of studies regarding artificial intelligence

Section	Requirements
Method
	Origin of dataset and description of the acquisition process
	Pre-processing methods
	Definition of ground truth
	Split of data set and should include a training, validation and test set. A clear statement that the test set is not used to tune hyperparameters or in the selection of the model
	Method and architecture used, whether it is pretrained or not, and what dataset it is pretrained on
	Full technical detail should be included in supplementary files
	Statement of post-selection analyses and why these are conducted
Results
	A complete report of all results including but not restricted to AUC, sensitivity, specificity, accuracy and kappa value for the overall model's performance and not for selected tasks
Discussion
	Risks of overfitting and bias
	Generalisability and cautions to take
	Clinical implementation

AUC: Area under the receiver operating characteristic curve.