Correction to: npj Digital Medicine https://doi.org/10.1038/s41746-023-00813-y, published online 12 April 2023

In this article the legend for Figure legends for 1 to 4 were incorrectly matched. The figure legends should have appeared as shown below. The original article has been corrected.

Fig. 1: The schematic diagram of all feature indexes and the framework of developing ENDOANGEL-ED.
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A Thirteen features, including seven deep learning-based features and six quantitative features. B The framework of developing ENDOANGEL-ED. HIS Hue, Saturation, Intensity.

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Fig. 2: The system interface of ENDOANGEL-ED.
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The prediction of the six feature indexes and the diagnostic result were presented on the left.

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Fig. 3: The performance of machine learning (ML) models and the weights of the included feature indexes.
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A The performance of the seven ML models on the internal image test set. Random forest (RF) showed the best performance. B Six indexes were determined by the RF model and the corresponding weights. RF random forest, GNB Gaussian Naive Bayes, KNN k-Nearest Neighbor, LR logistic regression, DT decision tree, SVM support vector machine, GBDT gradient boosting decision tree.

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Fig. 4: Performance of ENDOANGEL-ED and endoscopists in the internal and external videos.
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A Internal videos. B External videos.

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