The present invention relates to a method and
system for predicting the probability of
normal tissue damage based on
deep learning, belonging to the technical field of medical
data processing. The method includes: obtaining the tumor
dose data, basic information, and tumor diagnosis information of a patient; the tumor
dose data is formulated by a doctor based on the patient's tumor diagnosis information before radiotherapy for the patient; the basic information includes at least the patient's age; based on the tumor
dose data, obtaining the dose volume
histogram of the patient; based on the dose volume
histogram, obtaining the volume parameters and biological
equivalent dose of the patient's
normal tissue; inputting the volume parameters and biological
equivalent dose of the patient's
normal tissue, the patient's basic information, and tumor diagnosis information into a normal
tissue damage probability prediction model constructed based on
deep learning to obtain the damage probability of the patient's normal tissue. Compared with traditional statistical models or empirical formulas, the prediction accuracy of this method is higher, and it can more accurately evaluate the damage probability of normal tissue.