The present invention relates to an
artificial intelligence model and an implementation method therefor, which are capable of selecting and extracting, from CT images of patients with IgA
nephropathy, image features significant for
prognostic prediction and applying same to a
machine learning model to thereby predict, with high accuracy, the likelihood of the patients with IgA
nephropathy progressing to end-stage renal failure within five years. The present invention provides a
prognostic prediction model that rapidly predicts the prognosis of a patient without an invasive
kidney biopsy, overcomes the limitations of conventional
pathology diagnosis relying on invasive methods, and enables periodic prognostic evaluation with significantly improved reliability. In addition, the present invention is capable of precisely reflecting characteristics of each item and accurately predicting a
clinical course of a patient, by combining various
feature selection methods and binary classifiers for each item of mesangial hypercellularity (M), endothelial hypercellularity (E), segmental
glomerulosclerosis (S), and tubular
atrophy /
interstitial fibrosis (T), which constitute a MEST
score.