Table 7 Comprehensive assessment for H. pylori status by scSE-CatBoost classification models with endoscopic images from the antrum and body of same patients.

From: Application of artificial intelligence in endoscopic image analysis for the diagnosis of a gastric cancer pathogen-Helicobacter pylori infection

Method

Accuracy

Sensitivity

Specificity

PPV

NPV

AUC

scSE-CatBoost

0.90

1.00

0.81

0.82

1.00

0.88

  1. scSE, CatBoost, PPV, NPV and AUC are short forms for Spatial Squeeze and Channel Excitation Block, Categorical Boosting, Positive Predictive Value, Negative Predictive Value and Area Under the ROC curve.
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