Processing

Please wait...

Settings

Settings

Goto Application

1. WO2020223434 - CLASSIFYING NEUROLOGICAL DISEASE STATUS USING DEEP LEARNING

Publication Number WO/2020/223434
Publication Date 05.11.2020
International Application No. PCT/US2020/030617
International Filing Date 30.04.2020
IPC
A61B 5/04 2006.1
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes; Identification of persons
04Measuring bioelectric signals of the body or parts thereof
CPC
A61B 5/0042
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes
0033Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room
004adapted for image acquisition of a particular organ or body part
0042for the brain
A61B 5/055
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes
05Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
055involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
A61B 5/4088
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes
40Detecting, measuring or recording for evaluating the nervous system
4076Diagnosing or monitoring particular conditions of the nervous system
4088Diagnosing of monitoring cognitive diseases, e.g. Alzheimer, prion diseases or dementia
A61B 5/7267
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes
72Signal processing specially adapted for physiological signals or for diagnostic purposes
7235Details of waveform analysis
7264Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
7267involving training the classification device
G06K 9/6267
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
62Methods or arrangements for recognition using electronic means
6267Classification techniques
G06N 3/02
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
Applicants
  • THE TRUSTEES OF COLUMBIA UNIVERSITY IN THE CITY OF NEW YORK [US]/[US]
Inventors
  • FENG, Xinyang
  • PROVENZANO, Frank
  • SMALL, Scott, A.
Agents
  • GANGEMI, Anthony, P.
Priority Data
62/840,63330.04.2019US
63/017,30429.04.2020US
Publication Language English (en)
Filing Language English (EN)
Designated States
Title
(EN) CLASSIFYING NEUROLOGICAL DISEASE STATUS USING DEEP LEARNING
(FR) CLASSIFICATION D'ÉTAT DE MALADIE NEUROLOGIQUE À L'AIDE D'UN APPRENTISSAGE PROFOND
Abstract
(EN) A method for classifying neurological disease status is described. The method includes acquiring, by a data preprocessor logic, patient image data. The method further includes generating, by a trained artificial neural network (ANN), a classification output based, at least in part, on the patient image data. The classification output corresponds to a neurological disease status of the patient. The trained ANN is trained based, at least in part, on longitudinal source data.
(FR) L'invention concerne une méthode de classification d'un état de maladie neurologique. La méthode consiste à acquérir, par une logique de préprocesseur de données, des données d'image de patient. La méthode consiste en outre à générer, par un réseau neuronal artificiel (ANN) entraîné, une sortie de classification sur la base, au moins en partie, des données d'image de patient. La sortie de classification correspond à un état de maladie neurologique du patient. L'ANN entraîné est entraîné sur la base, au moins en partie, de données source longitudinales.
Latest bibliographic data on file with the International Bureau