公开(公告)号：US20190180878A1

公开(公告)日：2019-06-13

申请号：US16217152

申请日：2018-12-12

Applicant: UNIVERSITY OF SOUTH CAROLINA

Inventor： Julius Fridriksson ,  Christopher Rorden

IPC: G16H50/30 ,  G06N20/00 ,  G16H20/40

Abstract: The present disclosure provides systems and methods that include or otherwise leverage a machine-learned brain injury location model to predict locations of brain injury in a patient based on test data associated with the patient, such as, for example, behavioral test data. For example, the machine-learned brain injury location model can be trained on training data associated with a corpus of patients, where the training data includes sets of example test data (e.g., behavioral test data) respectively labeled with ground truth brain injury locations.

公开(公告)号：US11557400B2

公开(公告)日：2023-01-17

申请号：US17141436

申请日：2021-01-05

Applicant: UNIVERSITY OF SOUTH CAROLINA

Inventor： Julius Fridriksson ,  Christopher Rorden

IPC: G16H50/30 ,  G16H20/40 ,  G16H50/70 ,  G16H50/50 ,  G06N20/00 ,  A61N1/36 ,  A61B5/00 ,  A61B5/369

Abstract: The present disclosure provides systems and methods that include or otherwise leverage a machine-learned brain injury location model to predict locations of brain injury in a patient based on test data associated with the patient, such as, for example, behavioral test data. For example, the machine-learned brain injury location model can be trained on training data associated with a corpus of patients, where the training data includes sets of example test data (e.g., behavioral test data) respectively labeled with ground truth brain injury locations.

公开(公告)号：US10916348B2

公开(公告)日：2021-02-09

申请号：US16217152

申请日：2018-12-12

Applicant: UNIVERSITY OF SOUTH CAROLINA

Inventor： Julius Fridriksson ,  Christopher Rorden

IPC: G16H50/30 ,  G06N20/00 ,  G16H20/40 ,  G16H50/70 ,  G16H50/50 ,  A61N1/36 ,  A61B5/0476 ,  A61B5/00

Abstract: The present disclosure provides systems and methods that include or otherwise leverage a machine-learned brain injury location model to predict locations of brain injury in a patient based on test data associated with the patient, such as, for example, behavioral test data. For example, the machine-learned brain injury location model can be trained on training data associated with a corpus of patients, where the training data includes sets of example test data (e.g., behavioral test data) respectively labeled with ground truth brain injury locations.