A device and system for determining a sequence of blood vessel condition parameters of a blood vessel by utilizing a computer

Abstract

The invention relates to a device and system for determining a sequence of blood vessel condition parameters of a blood vessel by utilizing a computer. The device includes a memory, a processor and acomputer executable instruction stored in the memory and capable of running on the processor. The device is used for acquiring an image block sequence of a blood vessel path; and based on the acquiredimage block sequence of the blood vessel path, predicting a sequence of blood vessel condition parameters of the blood vessel path by utilizing a trained deep learning model formed by series connection of a data stream neural network, a recurrent neural network and a conditional random field model. By utilizing advantages of the recurrent neural network in sequence learning and advantages of a convolutional neural network in image learning, and through end-to-end model training, the device overcomes several critical problems in rapid computing of a blood vessel condition parameter sequence, such as computing speed increasing, manual intervention reducing for feature extraction and accuracy ensuring.

Description

technical field [0001] The present invention relates to artificial intelligence based learning network prediction and analysis of vascular condition parameters. More specifically, the present invention relates to an apparatus, system and medium for determining a sequence of parameters of a vascular condition on a blood vessel using a computer. Background technique [0002] Recently, in the medical field, various parameters of vascular conditions, including fractional flow reserve (FFR), such as but not limited to blood pressure and blood flow in blood vessels, provide important references for doctors in cardiovascular diagnosis. [0003] Taking FFR as an example, recent studies have shown that hemodynamic characteristics based on FFR are important indicators for determining and evaluating the best treatment for patients with arterial diseases. A large number of clinical trials have proved that the FFR value can well guide the treatment of coronary artery stenosis and other ...

AI Technical Summary

Problems solved by technology

However, computational simulations using computational dynamics methods will generate a large amount of computational burden and long computational time, making these virtual non-invasive measurements difficult to meet the requirements of clinical environments, which seriously hinders their application in clinical environments.

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