公开(公告)号：US11049244B2

公开(公告)日：2021-06-29

申请号：US16621800

申请日：2018-06-18

Applicant: Rensselaer Polytechnic Institute

Inventor： Ge Wang ,  Mannudeep Kalra ,  Juergen Hahn ,  Uwe Kruger ,  Wenxiang Cong ,  Hongming Shan

IPC: G06T7/00 ,  G06T7/11 ,  G06N3/08

Abstract: Computed tomography (CT) screening, diagnosis, or another image analysis tasks are performed using one or more networks and/or algorithms to either integrate complementary tomographic image reconstructions and radiomics or map tomographic raw data directly to diagnostic findings in the machine learning framework. One or more reconstruction networks are trained to reconstruct tomographic images from a training set of CT projection data. One or more radiomics networks are trained to extract features from the tomographic images and associated training diagnostic data. The networks/algorithms are integrated into an end-to-end network and trained. A set of tomographic data, e.g., CT projection data, and other relevant information from an individual is input to the end-to-end network, and a potential diagnosis for the individual based on the features extracted by the end-to-end network is produced. The systems and methods can be applied to CT projection data, MRI data, nuclear imaging data, ultrasound signals, optical data, other types of tomographic data, or combinations thereof.

公开(公告)号：US20200380673A1

公开(公告)日：2020-12-03

申请号：US16621800

申请日：2018-06-18

Applicant: Rensselaer Polytechnic Institute

Inventor： Ge Wang ,  Mannudeep Kalra ,  Juergen Hahn ,  Uwe Kruger ,  Wenxiang Cong ,  Hongming Shan

IPC: G06T7/00 ,  G06T7/11 ,  G06N3/08

Abstract: Computed tomography (CT) screening, diagnosis, or another image analysis tasks are performed using one or more networks and/or algorithms to either integrate complementary tomographic image reconstructions and radiomics or map tomographic raw data directly to diagnostic findings in the machine learning framework. One or more reconstruction networks are trained to reconstruct tomographic images from a training set of CT projection data. One or more radiomics networks are trained to extract features from the tomographic images and associated training diagnostic data. The networks/algorithms are integrated into an end-to-end network and trained. A set of tomographic data, e.g., CT projection data, and other relevant information from an individual is input to the end-to-end network, and a potential diagnosis for the individual based on the features extracted by the end-to-end network is produced. The systems and methods can be applied to CT projection data, MRI data, nuclear imaging data, ultrasound signals, optical data, other types of tomographic data, or combinations thereof.

公开(公告)号：US11699530B2

公开(公告)日：2023-07-11

申请号：US17413354

申请日：2019-12-11

Applicant: RENSSELAER POLYTECHNIC INSTITUTE

Inventor： Juergen Hahn ,  Troy Vargason ,  Uwe Kruger

IPC: G16H50/30 ,  G16H10/60

CPC classification number: G16H50/30 ,  G16H10/60

Abstract: Fisher discriminant analysis is performed on data sets of typically developing (TD) individuals and data sets of autism spectrum disorder (ASD) individuals to produce a model that classifies TD individuals from ASD individuals. The ASD data sets include pre-treatment folate-dependent one-carbon metabolism (FOCM) and transsulfuration (TS) pathway metabolic profile data and post-treatment folate-dependent one-carbon metabolism (FOCM) and transsulfuration (TS) pathway metabolic profile data for patients receiving one or more ASD treatments. Changes in adaptive behavior are predicted by utilizing regression of changes in adaptive behavior and changes in biochemical measurements observed in the data sets. Thus, the system can be used to predict the effectiveness of a given course of treatment for an ASD patient based on measured metabolite data of that patient, or to predict the overall effectiveness of a clinical trial based on metabolite data for the trial participants.

公开(公告)号：US20180358127A1

公开(公告)日：2018-12-13

申请号：US16002329

申请日：2018-06-07

Applicant: RENSSELAER POLYTECHNIC INSTITUTE

Inventor： Daniel Howsmon ,  Uwe Kruger ,  Juergen Hahn

IPC: G16H50/30

CPC classification number: G16H50/30

Abstract: Methods and systems for detecting an autism state are disclosed. A plurality of data arrays are received, each including a plurality of values. Each of the plurality of values represent a concentration of a different metabolite. A score for each of the plurality of data arrays is calculated based on a relationship between the plurality of values of each of the respective plurality of data arrays. The score for each of the plurality of data arrays is classified into an autism class and a neurotypical class. A test score for a test data array is calculated based on a relationship between the plurality of test values and can then be grouped into one of the autism class and the neurotypical class. The system thus can use biomarkers identified in a metabolic pathway, such as abnormalities in folate-dependent one-carbon metabolism (FOCM) and transsulfuration (TS), to identify patients with a high likelihood of having autism.