Patent ID: 11857292
Assignee: E8IGHT CO., LTD.
Field: Medical technology (Instruments)
Classification: CPC A  G | IPC A  G

Claim 7:
8. A vascular disease diagnosing apparatus, comprising:
one or more processors; and
a memory in which one or more programs executed by the one or more processors are stored,
wherein when the programs are executed by the one or more processors, the one or more processors perform operations including:
generating first geometric feature parameter learning data based on a predetermined synthetic model;
calculating first fractional flow reserve data and first flow feature data using the first geometric feature parameter learning data;
applying the first geometric parameter learning data, the first fractional flow reserve data, and the first flow feature data to Gaussian process regression analysis to generate a first learning model;
acquiring virtual biometric authentication data for a virtual patient model;
generating second geometric feature parameter learning data based on the virtual patient model;
applying the second geometric feature parameter learning data to the first learning model to obtain second fractional flow reserve data;
applying the second geometric feature parameter learning data to computational fluid dynamics (CFD) to obtain second flow feature data;
generating a second learning model based on the virtual biometric authentication data, the second fractional flow reserve data, the second flow feature data, information related to determining of a vascular disease, and information regarding whether to perform a surgery on the vascular disease;
acquiring patient information for a diagnosis subject;
generating geometric feature parameter information based on the patient information;
applying the geometric feature parameter information to the first learning model to calculate fractional flow reserve (FFR) information;
applying the geometric feature parameter information to computational fluid dynamics (CFD) to calculate flow feature information;
acquiring biometric authentication information of the diagnosis subject included in the patient information; and
applying the fractional flow reserve information, the flow feature information, and the biometric authentication information of the diagnosis subject to the second learning model to determine a vascular disease and determine whether to perform a surgery on the vascular disease.