BIOMARKER FOR EARLY DETECTION OF ALZHEIMER DISEASE
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IPC분류정보
국가/구분
United States(US) Patent
공개
국제특허분류(IPC7판)
A61B-005/00
G16H-030/40
G06N-020/00
A61B-005/055
A61B-005/16
출원번호
16724293
(2019-12-22)
공개번호
20210186409
(2021-06-24)
우선권정보
EM-EP19218311.9 (2019-12-19)
발명자
/ 주소
Lee, Gwo Giun
Kung, Te-Han
Chao, Tzu-Cheng
Kuo, Yu-Min
Tsai, Meng-Ru
출원인 / 주소
Lee, Gwo Giun
인용정보
피인용 횟수 :
0인용 특허 :
0
초록▼
The present disclosure relates to a method for providing biomarker for early detection of Alzheimer's Disease (AD), and particularly to a method that is able to enhance the accuracy of predicting AD from Mild Cognitive Impairment (MCI) patients using the Hippocampus magnetic resonance imaging (MRI)
The present disclosure relates to a method for providing biomarker for early detection of Alzheimer's Disease (AD), and particularly to a method that is able to enhance the accuracy of predicting AD from Mild Cognitive Impairment (MCI) patients using the Hippocampus magnetic resonance imaging (MRI) scans and Mini-Mental State Examination (MMSE) data. The providing MRI images containing the anatomical structure of Hippocampus biomarker and MMSE data as a training data set; training a processor using the training data set, and the training comprising acts of receiving MRI images and MMSE data as a testing data set from a target; and classifying the test data by the trained processor to include aggregating predictions.
대표청구항▼
1. A method of providing biomarker for early detection of Alzheimer's Disease, comprising: providing magnetic resonance imaging (MRI) images containing the anatomical structure of Hippocampus and Mini-Mental State Examination (MMSE) data as a training data set; training a processor using the trainin
1. A method of providing biomarker for early detection of Alzheimer's Disease, comprising: providing magnetic resonance imaging (MRI) images containing the anatomical structure of Hippocampus and Mini-Mental State Examination (MMSE) data as a training data set; training a processor using the training data set, and the training comprising acts of proceeding an MRI image preprocessing to determine volume of a Hippocampus of each MRI images, and segmenting the Hippocampus into sections;determining surface areas for each section of each Hippocampus in MRI images;determining a Ratio of Principle Curvature (RPC) for each section of each Hippocampus; andselecting candidate parameters as inputs to iteratively train an iterative neural network in the processor, wherein the candidate parameters are selected from the volume of Hippocampus, the surface areas and the PRC of sections of Hippocampus, and scores of MMSE data;receiving MRI images and MMSE data as a testing data set from a target; andclassifying the test data by the trained processor to include aggregating predictions. 2. The method as claimed in claim 1, wherein the act of proceeding an MRI image preprocessing to determine volume of a Hippocampus of each MRI images and segmenting the Hippocampus into sections, comprises an intensity normalization, linear stereotaxic registration, creating linear mask, linear classification and linear segmentations. 3. The method as claimed in claim 2, wherein the segmentations of the Hippocampus are alveus, parasubiculum, presubiculum, subiculum, CA1, CA2/3, CA4, GC-DG, HATA, fimbria, molecular layer, Hippocampus fissure and Hippocampus tail. 4. The method as claimed in claim 3, wherein the candidate parameter comprises values of Hippocampus volume, Subiculum surface area, CA1 surface area, CA3 surface area, and the average RPCs of Subiculum, CA1 and CA3. 5. The method as claimed in claim 1, wherein the candidate parameter comprises scores of orientations, attention, recall and language in the MMSE data. 6. The method as claimed in claim 1, wherein the act of classifying the MRI images and MMSE data by the trained processor are proceed in different iterative neural networks. 7. The method as claimed in claim 1, wherein the act of determining surface areas for each section of each Hippocampus in MRI images comprises acts of reconstructing the each identified Hippocampus with sections to build a 3 Dimensions (3D) Hippocampus model, smoothing the surface area of the 3D Hippocampus model; and calculating the surface areas of each sections of Hippocampus. 8. A method for quantifying the anatomical structure of a Hippocampus as biomarker for predicting Alzheimer's Disease, and the method comprising: receiving MRI images of a brain scan;preprocessing the MRI images to identify the Hippocampus for determining volumes of each identified Hippocampus;segmenting each identified Hippocampus into multiple sections;reconstructing the each identified Hippocampus with sections to build a 3D Hippocampus model; smoothing the surface area of the 3D Hippocampus model;calculating a surface area of each sections of each identified Hippocampus; andcalculating a maximum curvature and a minimum curvature of each sections of each identified Hippocampus to determine an RPC. 9. The method as claimed in claim 8, wherein the acts of preprocessing and segmenting the MRI images is proceed by a computer using FreeSurfer. 10. The method as claimed in claim 8, wherein the 3D Hippocampus model is built by Marching cubes. 11. The method as claimed in claim 8, wherein the act of smoothing the surface area of the 3D Hippocampus model is achieved by Laplacian smoothing.
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