The invention discloses an
interstitial lung disease diagnosis method and
system based on
artificial intelligence, and the method comprises the steps: carrying out the structural
processing of clinical data of a patient, constructing a multi-dimensional
hypergraph structure, inputting a variational graph auto-
encoder model under a constraint condition, carrying out the coding, and introducing a
pathological feature constraint to generate a hidden variable; extracting high-order features by using an
artificial intelligence multi-dimensional
hypergraph neural network, and inputting the high-order features into a diagnosis model based on a deep neural network for optimization training; and finally,
processing to-be-detected data according to the same process, calculating a diagnosis probability value, and outputting a result in combination with a threshold value. The
system comprises six units including a data structured
processing unit, a coding
feature extraction unit, a
feature aggregation unit, a model training optimization unit, a diagnosis reasoning unit and a result generation unit which are mutually connected and cooperatively work. Through multi-dimensional
hypergraph modeling and a
pathological constraint mechanism, the problems of poor multi-
modal data fusion and lack of
pathological feature constraints of a traditional method are effectively solved, and efficient and accurate diagnosis of interstitial
lung diseases is realized.