公开(公告)号：EP2743852A4

公开(公告)日：2015-04-15

申请号：EP12820286

申请日：2012-07-31

Applicant: UNI AUT NOMA DE BARCELONA ,  MASARYK UNIVERSITY

Inventor： VALIENTE MALMAGRO MANUEL ,  LÓPEZ MESAS MONTSERRAT ,  ALONSO SÁNCHEZ AGUSTÍN ,  HAVEL JOSEF

IPC: G06F19/24 ,  G01N33/84

CPC classification number: G01N33/84 ,  G01N2800/345 ,  G06F19/24

公开(公告)号：EP2696191A4

公开(公告)日：2014-10-22

申请号：EP12767459

申请日：2012-04-10

Applicant: UNI AUT NOMA DE BARCELONA ,  MASARYK UNIVERSITY ,  UNIVERSIT LA SAPIENZA

Inventor： BLANCO FRANCISCO ,  BONIFAZI GIUSEPPE ,  GARGIULO ALDO ,  HAVEL JOSEF ,  LÓPEZ MONTSERRAT ,  SERRANTI SILVIA ,  VALIENTE MANUEL

IPC: G01N21/00 ,  A61B5/20 ,  A61B6/00 ,  G01J3/40 ,  G06F19/00 ,  G06T1/40

CPC classification number: A61B5/4842 ,  A61B5/201 ,  G01J3/2823 ,  G01N21/359 ,  G01N2201/129 ,  G01N2201/1296 ,  G06K9/6247 ,  G16H50/20

Abstract: Method for the characterisation and classification of kidney stones, comprised the following stages: (a) Taking a series of kidney stone samples and cutting them to observe their interior, obtaining the flattest possible surface, (b) The technique of Hyperspectral Imaging (HSI) is applied to obtain the spectra of previously cut kidney stones, selecting a series of Regions of Interest (ROI) and analysing the image using Principal Component Analysis (PCA) (c) The main species are identified using Factor Analysis (FA) (d) Outliers are identified using Principal Component Analysis (PCA) (e) The different types of kidney stones are analysed using Principal Component Analysis (PCA) (f) The data obtained from the Principal Component Analysis (PCA) are subject to Artificial Neural Networks (ANN) for classification.

公开(公告)号：EP2743852A1

公开(公告)日：2014-06-18

申请号：EP12820286.8

申请日：2012-07-31

Applicant: Universitat Autònoma De Barcelona ,  Masaryk University

Inventor： VALIENTE MALMAGRO, Manuel ,  LÓPEZ MESAS, Montserrat ,  ALONSO SÁNCHEZ, Agustín ,  HAVEL, Josef

IPC: G06F19/24

CPC classification number: G01N33/84 ,  G01N2800/345 ,  G06F19/24

Abstract: Use of artificial neural networks for detecting the formation of kidney stones and identifying the chemical composition of such stones, which comprises a multivariate analysis and artificial neural networks (ANN) on a database including from 10 to 14 metabolic markers in urine as analysis parameters selected from the group consisting of urine volume, pH, creatinine levels, uric acid, urea, sodium, potassium, chloride, citrate, calcium, oxalate, magnesium, phosphate and proteinuria.

Abstract translation: 为了检测肾结石的形成和确定检查结石的化学成分，从选择的分析参数，包括数据库，从尿中10至14代谢标记物，包括在多变量分析和人工神经网络（ANN）利用人工神经网络 选自由尿量，pH值，肌酐水平，尿酸，尿素，钠，钾，氯化物，柠檬酸盐，钙，草酸盐，镁，磷酸和蛋白尿。

公开(公告)号：EP2696191A1

公开(公告)日：2014-02-12

申请号：EP12767459.6

申请日：2012-04-10

Applicant: Universitat Autònoma De Barcelona ,  Masaryk University ,  Università La Sapienza

Inventor： BLANCO, Francisco ,  BONIFAZI, Giuseppe ,  GARGIULO, Aldo ,  HAVEl, Josef ,  LÓPEZ, Montserrat ,  SERRANTI, Silvia ,  VALIENTE, Manuel

IPC: G01N21/00 ,  G06T1/40 ,  G01J3/40 ,  G06F19/00 ,  A61B6/00

CPC classification number: A61B5/4842 ,  A61B5/201 ,  G01J3/2823 ,  G01N21/359 ,  G01N2201/129 ,  G01N2201/1296 ,  G06K9/6247 ,  G16H50/20

Abstract: Method for the characterisation and classification of kidney stones, comprised the following stages:
(a) Taking a series of kidney stone samples and cutting them to observe their interior, obtaining the flattest possible surface,
(b) The technique of Hyperspectral Imaging (HSI) is applied to obtain the spectra of previously cut kidney stones, selecting a series of Regions of Interest (ROI) and analysing the image using Principal Component Analysis (PCA)
(c) The main species are identified using Factor Analysis (FA)
(d) Outliers are identified using Principal Component Analysis (PCA)
(e) The different types of kidney stones are analysed using Principal Component Analysis (PCA)
(f) The data obtained from the Principal Component Analysis (PCA) are subject to Artificial Neural Networks (ANN) for classification.

Abstract translation: 肾结石的表征和分类方法包括以下几个阶段：（a）取一系列肾结石样本并切割它们观察它们的内部，获得最平坦的表面;（b）高光谱成像技术（HSI） 用于获得先前切开的肾结石的光谱，选择一系列感兴趣区域（ROI）并使用主成分分析（PCA）分析图像（c）使用因子分析（FA）确定主要种类（d） （e）使用主成分分析（PCA）分析不同类型的肾结石（f）从主成分分析（PCA）获得的数据受到人工神经网络（人工神经网络 ）进行分类。