The Charles Stark Draper Laboratory, Inc.

The Brigham and Women's Hospital, Inc.

Science Applications International Corporation

The Charles Stark Draper Technology, Inc.

John M Irvine

Richard Wood

Laura Jane Mariano

Nathan Lowry

Rami S Mangoubi

Mark E Abramson

Abraham Schneider

Peter Lommel

Steven A Israel

Seamus Tuohy

Lei Hamilton

Alexander P Lin

Nirmal Keshava

David Reed

Mark D Wiederhold

Rodney P Meyer

Lev E Givon

David Floyd

Stephen Duncan

A MRS (magnetic resonance spectroscopy or nuclear magnetic resonance NMR)-based PTSD (post-traumatic stress disorder) and mTBI (mild traumatic brain injury) diagnostic system and method uses MRS signals, already pre-processed by the MRS scanner software. The signals are collected in vivo from specific regions of the brain. A wavelet decomposition is applied to the MRS signals, and the amplitude of the wavelet coefficients and their location in the MRS signals are used as features for training diagnostic classifiers of disease states. These classifiers are identified through analysis of features of individuals whose health status is known. Once the classifiers are trained, patients can be diagnosed by using the same wavelet features extracted from in vivo MRS scans of their brain regions.

Method and system for post-traumatic stress disorder (PTSD) and mild traumatic brain injury (mTBI) diagnosis using magnetic resonance spectroscopy

A method and a system for analysis of raw MRS data, in the form of signal strength versus chemical shift (ppm), from multiple scanners, includes 'signal estimation' from each raw data set, followed by cross-scanner 'data harmonization' of results. The final resulting MRS signals are consistent from one scanner to another, and are used for analysis by radiologists and other physicians.

Method and procedure for signal estimation and data harmonization for magnetic resonance spectroscopy (MRS)

A method of predicting progression of a cognitive state in a subject is disclosed including obtaining a neuroimage of the subject, acquiring a data sample from the neuroimage, selecting a time at which to predict progression of the cognitive state, and performing a calculation on the data sample by a transformation function to determine data associated with multiple cognitive metrics. A method of evaluating a cognitive state of a subject is also disclosed including providing a convolutional neural network, training the convolutional neural network with reference data to construct a transformation function, using the transformation function to predict multiple cognitive metrics from a subject data sample and a selected time, and determining the cognitive state of the subject from the predicted cognitive metrics. A cognitive evaluation system is also disclosed including a memory, a processor, and a cognitive state prediction component configured to program the processor with a transformation function.

Deep learning architecture for cognitive examination subscore trajectory prediction in Alzheimer's disease

The present disclosure describes a system and method to classify optical coherence tomography (OCT) images. The present system can classify OCT images without first segmenting the retina tissue. The system can generate one or more profiles from vertical transects through the OCT images. The system can identify image statistics based on the one or more profiles. The system's classifier can then classify the OCT images based on the identified image statistics.

System and method for classifying and quantifying age-related macular degeneration

Methods and systems are disclosed for obtaining and analyzing information to determine an estimated state of a real world. First information is obtained from a first source of a plurality of sources (e.g., data stores, real-time data streams, or information services), where the first information is potentially indicative of first and second states of the real world. If the first information exceeds a first decision threshold, then the estimated state is the first state, and if the first information exceeds a second decision threshold, then the estimated state is the second state. If the first information does not exceed either of the first and second decision thresholds, then second information is obtained from a second source. Obtaining the second information is based on whether the second source includes information that is relevant to the estimated state and whether the second source includes information that is redundant with the first information.

Method and system for obtaining and analyzing information from a plurality of sources

The present disclosure describes a system and method to segment optical coherence tomography (OCT) images. The present system uses a hybrid method that employs both Bayesian level sets (BLS) and graph-based segmentation algorithms. The system first identifies retinal tissue within an OCT image using the BLS algorithms. The identified retinal tissue is then further segmented using the graph-based segmentation algorithms.

System and method for the segmentation of optical coherence tomography slices

This disclosure provides systems, methods, and apparatus for analyzing a video are disclosed. An input video signal can be received and a quality metric associated with the input video signal can be determined. A parameter to be used in a first object tracking process can be selected, based in part on the quality metric associated with the input video signal. A background model can be extracted from the input video signal based on an online median estimate of the intensity values of the pixels in the image frames. The online median estimate can be calculated using a quantile function.

Systems and methods for detecting and tracking objects in a video stream

Methods involving extraction of information from the inherent variability of physiometrics, including data on cardiovascular and pulmonary functions such as heart rate variability, characteristics of ECG traces, pulse, oxygenation of subcutaneous blood, respiration rate, temperature or COcontent of exhaled air, heart sounds, and body resonance, can be used to identify individual subjects, particularly humans. Biometric data for use in the methods can be obtained either from contact sensors or at a distance. The methods can be performed alone or can be fused with previous identification algorithms.

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Somerville, MA, United States of America

John M Irvine