Tumor malignant risk stratification auxiliary diagnosis system of artificial intelligence medical image

Abstract

The invention discloses a tumor malignant risk stratification auxiliary diagnosis system of an artificial intelligence medical image. The system comprises a data acquisition module, a data preprocessing module, a model establishment module, a model verification and optimization module, a stratification diagnosis module and a database platform. The tumor malignant risk stratification auxiliary diagnosis system of the invention is based on artificial intelligence technology, successive stratification of the malignant risk of the tumor can be achieved, the clinical diagnosis thinking is simulated, based on the high-precision ability of detecting benign lesions and malignant tumors of artificial intelligence model, and the space-occupying lesions with definite imaging features are diagnosed automatically. As a result, the system can substantially assist the clinical management decision of space-occupying lesions, improve the existing work flow of clinical diagnosis, increase the confidenceof doctors in diagnosis, reduce the work pressure, reduce the anxiety of patients with low-risk malignant lesions, greatly improve the diagnostic rate of benign lesions and malignant tumors, and hopefully realize the landing implementation of artificial intelligence clinical auxiliary diagnosis.

Description

technical field [0001] The invention relates to a system and method for auxiliary diagnosis of tumor malignancy risk stratification, in particular to a system and method for auxiliary diagnosis of tumor malignancy risk stratification of artificial intelligence medical imaging; it belongs to the technical field of artificial intelligence auxiliary diagnosis of medical imaging. Background technique [0002] Tumor refers to the new organism formed by the proliferation of local tissue cells under the action of various tumorigenic factors. Most benign lesions do not require surgical intervention without mass effect. The prognosis of early surgical intervention of malignant tumors is usually good, while the prognosis of advanced malignant tumors is generally poor whether it is surgical intervention or radiotherapy and chemotherapy. At present, medical institutions around the world are continuously investing huge sums of money in the study of the occurrence, development and treatm...

AI Technical Summary

Problems solved by technology

[0005] In general, most of the current systems use public datasets or single-center datasets for training, and the generalization and robustness of the models need to be tested; at the same time, the quality of the data is uneven, and some data even have no clear pathological diagnosis The information, data reliability and effective sample size of the data have been questioned; in addition, most of the current systems use binary classification thinking, pursuing high accuracy or high AUC (area under the ROC curve), but this classification method based on the possibility of malignancy does not Considering the actual situation of clinical application, it cannot effectively provide a reference for the clinical decision-making management of space-occupying lesions

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