The General Hospital Corporation

The Brigham and Women's Hospital, Inc.

Ge Precision Healthcare LLC

Partners Healthcare System, Inc.

Michael Lev

John Francis Kalafut

Bernardo Canedo Bizzo

Synho Do

Giorgio Bonmassar

Ramon Gilberto Gonzalez

Mark Heinz Michalski

Christopher P Bridge

Markus Daniel Herrmann

James Hillis

Romane Gauriau

Charles J Lu

Eric S Rosenthal

Systems and techniques that facilitate automated localization of large vessel occlusions are provided. In various embodiments, an input component can receive computed tomography angiogram (CTA) images of a patient's brain. In various embodiments, a localization component can determine, via a machine learning algorithm, a location of a large vessel occlusion (LVO) in the patient's brain based on the CTA images. In various instances, the location of the LVO can comprise a laterality and an occlusion site. In various aspects, the laterality can indicate a right side or a left side of the patient's brain, and the occlusion site can indicate an internal carotid artery (ICA), an M1 segment of a middle cerebral artery (MCA) or an M2 segment of an MCA. In various cases, a visualization component can generate and display to a user a three-dimensional maximum intensity projection (MIP) reconstruction of the patient's brain based on the CTA images to facilitate visual verification of the LVO by the user.

CTA large vessel occlusion model

Systems and methods for rapid, accurate, fully-automated, brain hemorrhage deep learning (DL) based assessment tools are provided, to assist clinicians in the detection & characterization of hemorrhages or bleeds. Images may be acquired from a subject using an imaging source, and preprocessed to cleanup, reformat, and perform any needed interpolation prior to being analyzed by an artificial intelligence network, such as a convolutional neural network (CNN). The artificial intelligence network identifies and labels regions of interest in the image, such as identifying any hemorrhages or bleeds. An output for a user may also include a confidence value associated with the identification.

Systems and methods for brain hemorrhage classification in medical images using an artificial intelligence network

Systems and techniques for generating and/or employing a computed tomography (CT) medical imaging stroke model are presented. In one example, a system employs a convolutional neural network to generate learned medical imaging stroke data regarding a brain anatomical region based on CT data associated with the brain anatomical region and diffusion-weighted imaging (DWI) data associated with one or more segmentation masks for the brain anatomical region. The system also detects presence or absence of a medical stroke condition in a CT image based on the learned medical imaging stroke data.

Computed tomography medical imaging stroke model

Systems and methods for determining brain health of a subject include or employ an electrical stimulator configured to apply a current to at least one pair of electrodes, and the electrodes are positioned on a skull of the subject to apply the current and to receive brain activity of the subject. The electrical stimulator is configured to apply a current having a waveform according to a Stochastic Gabor Function (SGF). A signal processor is configured to record the brain activity of the subject in the form of spectral electrical impedance data, and a computer system having non-transient computer readable media is programmed and configured to process the spectral electrical impedance data and indicate an impedance change within the brain of the subject.

System and method for electrical impedance spectroscopy

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Newton, MA, United States of America

Michael Lev