Peripheral arterial disease diagnosis method based on deep learning

Abstract

The invention discloses a peripheral arterial disease diagnosis method based on deep learning. According to the method, tissue blood flow change data of peripheral arterial disease patients and healthy volunteers can be acquired by utilizing the diffusion related spectrum technology against the defects of a traditional method for diagnosing peripheral arterial diseases based on ABI; the tissue blood flow data is trained based on the proposed deep learning network, key features containing peripheral arterial disease information are extracted and are trained to obtain a deep learning network model for diagnosing peripheral arterial diseases, and test set data is input into a peripheral arterial disease diagnosis model for diagnosing, so that peripheral artery diseases are diagnosed. The method overcomes the defects of existing ABI diagnosed peripheral arterial diseases, verifies the feasibility of tissue blood flow measurement for peripheral arterial disease diagnosis, and provides a newtechnology and new method for peripheral arterial disease diagnosis.

Description

technical field [0001] The invention relates to the technical field of biomedical engineering, in particular to a method for diagnosing peripheral artery disease based on deep learning. Background technique [0002] Peripheral Arterial Disease (PAD) is a common cardiovascular disease, with intermittent claudication as the main symptom, and generally refers to systemic arterial stenosis or occlusion lesions other than coronary arteries and intracranial arteries. more than four times [1-2] . Clinical and epidemiological research results show that the incidence of cardiovascular and cerebrovascular complications in patients with peripheral artery disease is 2 to 4 times that of people without peripheral artery disease of the same age [3] . A large number of studies have proved that the change of blood flow in peripheral arterial disease is earlier than the change of structure, and the diagnosis and evaluation of the degree of blood flow change are particularly important for ...

AI Technical Summary

Problems solved by technology

[0014] In order to solve the problem that the traditional ABI detection method is inaccurate, requires manual calculation, and consumes time and energy, the present invention provides a method for diagnosing peripheral arterial disease based on deep learning

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