公开(公告)号：US20230316507A1

公开(公告)日：2023-10-05

申请号：US18040930

申请日：2021-08-26

Applicant: MEDIAN TECHNOLOGIES

Inventor： Nozha BOUJEMAA ,  Benoit HUET ,  Vladimir GROZA ,  Danny FRANCIS

IPC: G06T7/00 ,  G16H30/40 ,  G16H50/30

CPC classification number: G06T7/0012 ,  G16H30/40 ,  G16H50/30 ,  G06T2207/20084 ,  G06T2207/30096

Abstract: A method of patient stratification between respondents and non-respondents to immuno-oncology (IO). This method, based on deep-learned features extracted owing to automatic AI-based models that have been fully-trained, goes beyond traditional radiomic standards, opening new perspective for a broader uptake of machine learning solutions in both patient care and drug development. Based on latest Machine Learning advances, the here proposed method allows predicting non-invasively a patient's tumor response to immuno-oncology therapy based treatment. The here proposed method operates not only on early stage conditions though a whole organ and lesion-agnostic analysis for prediction, but also on advanced metastatic stages through a multi-organ analysis performing a disease-agnostic and stage-agnostic prediction, potentially in accordance with response criteria defined by the RECIST 1.1 evaluation methodology.

公开(公告)号：US20220414870A1

公开(公告)日：2022-12-29

申请号：US17772881

申请日：2020-11-05

Applicant: MEDIAN TECHNOLOGIES

Inventor： Elton REXHEPAJ ,  Corinne RAMOS ,  Nozha BOUJEMAA ,  Jean-Christophe BRISSET ,  Pierre BAUDOT ,  Sébastien POULLOT ,  Benjamin RENOUST ,  Benoit HUET

IPC: G06T7/00 ,  G06T7/11

Abstract: A method for performing classification of the severity of at least one liver disease from non-invasive radiographic images is disclosed. The method includes: providing radiographic images of slices of the abdomen of a patient; pre-processing the radiographic images by: segmenting liver and spleen, thus achieving a spleen binary mask and a liver binary mask per slice, and normalizing the images with each other, thus achieving normalized radiographic images per slice; for each slice, from the liver binary mask and the normalized radiographic images, extracting a liver parameter; from at least one spleen binary mask, extracting a spleen parameter; and classifying, in function of both parameters and by help of a trained Machine Learning model, the severity of liver disease between one among a group of liver disease at early stage and a group of liver disease at advanced stage.