Graded diagnosis of Helicobacter pylori infection using hyperspectral images of gastric juice

Helicobacter pylori is a potential underlying cause of many diseases. Although the Carbon 13 breath test is considered the gold standard for detection, it is high cost and low public accessibility in certain areas limit its widespread use. In this study, we sought to use machine learning and deep learning algorithm models to classify and diagnose H. pylori infection status. We used hyperspectral imaging system to gather gastric juice images and then retrieved spectral feature information between 400 and 1000 nm. Two different data processing methods were employed, resulting in the establishment of one‐dimensional (1D) and two‐dimensional (2D) datasets. In the binary classification task, the random forest model achieved a prediction accuracy of 83.27% when learning features from 1D data, with a specificity of 84.56% and a sensitivity of 92.31%. In the ternary classification task, the ResNet model learned from 2D data and achieved a classification accuracy of 91.48%. We applied hyperspectral imaging technology and artificial intelligence algorithms to rapidly and noninvasively diagnose the status of Helicobacter pylori infection by detecting gastric juice. We achieved relatively high accuracy for binary and ternary classification through small‐scale experiments.