Application of artificial intelligence deep learning algorithm to analyze the relationship between hormone sensitivity of idiopathic interstitial pneumonia and imaging features of high resolution CT.
Methods: the medical records and chest high-resolution CT images of patients with idiopathic interstitial pneumonia admitted to the respiratory department of the Third Hospital of Peking University from June 1, 2012 to December 31, 2020 were retrospectively analyzed.Application of artificial intelligence deep learning neural convolution network method to create recognition technology of different imaging features.Including ground glass, mesh, honeycomb, nodule or consolidation, the model was established. IIP patients were divided into hormone sensitive group and hormone insensitive group according to whether the use of hormone was effective or not.Logistic regression analysis was used to analyze the correlation between statistically significant parameters and hormone sensitivity.Artificial intelligence was used to establish the correlation model between imaging features and clinical data and hormone sensitivity.
Study Type
OBSERVATIONAL
Enrollment
150
Ground glass，honeycomb，reticulation, consolidation
Peking University Third Hospital
Beijing, Beijing Municipality, China
clinical data and imaging feature ratios in both groups
clinical data including ages，gender，symptoms,signs,smoking history,complications,laboratory examination，lung function. Imaging feature including ground-glass opacity, reticulation, honeycomb and consolidation.
Time frame: 3-6 months after medication
the relationship between imaging feature ratios and hormone sensibility
Logistic regression analyzing the relationship between imaging feature ratios and hormone sensibility.
Time frame: 3-6 months after medication
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