The invention belongs to the technical field of medical
image detection, and particularly relates to an esophageal
flora image intelligent detection method for risk prediction of gastroesophageal
reflux disease, which comprises the following steps: acquiring
flora images and clinical data, and constructing a
perception network; quantifying
flora displacement characteristics by using
image registration; the method comprises the following steps of: extracting flora density, aggregation degree and
fluorescence gradient characteristics by combining improved U-Net + + segmentation, fusing flora and epithelial
cell lesion characteristics through space-time attention, generating a flora-host association map, constructing a flora dynamic evolution model by utilizing a space-
time map convolutional network, predicting Barrett esophageal
occurrence probability and flora
diffusion trend, and predicting the
occurrence probability of the Barrett
esophagus. Calculating a
lesion risk level through a flora imbalance quantification
algorithm; in combination with
reflux frequency and pH fluctuation, a flora-environment
interaction model is used for simulating a field planting coefficient and correcting parameters, the parameters are transmitted to a diagnosis platform to deduce canceration risks, grading suggestions are triggered, and a visual report is generated. Therefore, the problems of long detection period, weak microscopic analysis capability and the like in the prior art are solved.