Machine learning preoperative positioning method based on generalized linear model

A generalized linear model and machine learning technology, applied in the field of medical assistance, can solve problems such as time-consuming subjectivity and the influence of physiological signal noise

Active Publication Date: 2020-06-05
THE FIRST AFFILIATED HOSPITAL OF MEDICAL COLLEGE OF XIAN JIAOTONG UNIV
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AI Technical Summary

Problems solved by technology

However, the ICA method requires manual selection of important brain function components based on visual inspection
Therefore, interpretation of ICA is time-consuming and subjective, and can be affected by physiological signal noise

Method used

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  • Machine learning preoperative positioning method based on generalized linear model
  • Machine learning preoperative positioning method based on generalized linear model
  • Machine learning preoperative positioning method based on generalized linear model

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[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0027] according to figure 1 As shown, the present embodiment proposes a preoperative positioning method based on generalized linear model machine learning, including the following steps:

[0028] Step 1: Obtain structural and fMRI images

[0029] Use the spoiled gradient echo sequence 3D-T1 weighted sequence to obtain the structural image, and then obtain the functional magnetic resonance image through the T2* weighted single gradient echo planar imaging seq...

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Abstract

The invention discloses a machine learning preoperative positioning method based on a generalized linear model. The machine learning preoperative positioning method comprises the following steps thatobtaining a structure image and a functional magnetic resonance image; preprocessing the structure image and the functional magnetic resonance image; segmenting and registering the structure image andthe functional magnetic resonance image, and extracting a motion network by using a double regression method; constructing a generalized linear prediction model, and performing generalized linear prediction model fitting on brain partitions; according to the method, the motion activation graph of the motion area prediction individual can be more accurately identified on the basis of resting statefunctional magnetic resonance, and passive task activation can be effectively predicted by using a generalized linear prediction model trained by active task activation with actual task functional magnetic resonance image activation as a reference; the generalized linear prediction model has important clinical application value for patients who cannot achieve satisfactory task performance, including old people, children and tumor patients.

Description

technical field [0001] The invention relates to the field of medical assistance technology, in particular to a preoperative positioning method based on generalized linear model machine learning. Background technique [0002] Accurate positioning of brain functional areas is crucial for preoperative planning and surgical path planning. Preoperative positioning not only helps to maximize tumor resection, but also minimizes postoperative neurological deficits, thereby improving the quality of life of patients after surgery. Intraoperative electrical cortical stimulation (ECS), as the gold standard for clinical localization of important functional brain regions, provides an important aid during surgery. But ECS is an invasive technique that requires the expertise of the surgical team and the active cooperation of the patient. Furthermore, this approach often results in prolonged operative time and increases the incidence of intraoperative seizures. In the past two decades, bl...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/055
CPCA61B5/0033A61B5/0042A61B5/055
Inventor 牛晨张秋丽张明任雨寒温鑫刘翔
Owner THE FIRST AFFILIATED HOSPITAL OF MEDICAL COLLEGE OF XIAN JIAOTONG UNIV
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