Rapid, accurate, clinical decision support

Advanced digital signal processing of brain electrical activity data at the core of FDA cleared AI, machine learning derived algorithms, empower clinicians to rule out likelihood of intracranial hemorrhage & objectively assess for concussion

 

Science & Tech hero-Machine Learning

How BrainScope Works : Use of EEG based brain biomarkers to assess head injured patients and assist clinicians in their diagnosis

Exceptionally well validated 

12 years of development funded in part by 8 Department of Defense studies and 2 GE/NFL Head Challenge grants

 

Sensitivity well above that of commonly used diagnostic tools for other medical conditions

sensitivity

 

BrainScope Structural Injury Classifier (SIC) was demonstrated to objectively identify the likelihood of an intracranial hemorrhage with 99% sensitivity to even the smallest amount of detectable blood (≥1 mL). 

It is rocket science, here's what makes it work

 

Hardware

 

 

Device with negative SIC

 

The handheld medical device acquires  brain electrical activity data recorded from the proprietary 8-electrode disposable headset

Brain Electrical Activity  Database

 

EEG Database

 

 

Advanced signal processing captures changes in brain activity distinctive of TBI including measures that reflect disruption in neuronal transmission between brain regions (connectivity), disorganization of neural networks & changes in neurotransmitters

AI Derived Biomarker Algorithms

 

tSNE plot

 

 

 

With brain electrical activity features as core inputs to machine learning classifier building methods, distinctive profiles of TBI are identified

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Curious about our FDA cleared algorithms, research, or published papers? 

Current assessments on the BrainScope device

BrainScope’s technology records brain electrical activity in a conventional way. Incorporating advanced signal processing, the data is then quantified as inputs to derive machine learning algorithms. The three FDA cleared, AI derived algorithms produce easy to read, actionable results.*
 
*No neurologist or electroencephalographer required
 

SIC with Blue Background

Structural Injury Classifier (SIC)

A multimodal AI derived algorithm that indicates the likelihood of being negative for brain bleed on a CT scan and identifies the need for further evaluation

BFI Screen@2x

Brain Function Index (BFI) textpadding

A brain electrical activity based algorithm for the assessment of brain function impairment, obtained from the same recording used to compute the SICcan aid in early clinical diagnosis of concussion and referrals 

CI Screen@2x

Concussion Index (CI)

An objective multimodal AI derived algorithm with brain electrical activity at its core—aids in clinical diagnosis of concussion

 

 

Intersection 4

 
 
Digitized & Neurocognitive Clinical Assessments

Includes assessments commonly used by clinicians to assess head injured patients, including PECARN Decision Rule for pediatrics 

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Interested in collaborating?

Our research team

Led by our Chief Scientific Officer, Leslie S. Prichep, PhD, our vibrant research group has experience beyond traumatic brain injury into neurological conditions such as stroke, Alzheimer's disease, depression, and cognitive decline.

 

 

The BrainScope algorithms were developed by applying advanced AI/machine learning technology to extensive patient data, including EEG data, symptoms, CT scan and neurological test results. As the database grows, machine learning capabilities can identify additional data patterns, enhancing future technology, advancing the potential clinical application of head injury assessment, and identifying new indications for use, using the neurotechnology platform that has been developed by BrainScope over the past decade.  

Dr. Prichep and her team continue to work on furthering our understanding of brain health.

 

 

 

Science Team

 

Our research partners

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University of Rochester Medical Center

Jeffrey J Bazarian, MD
 
Department of Emergency Medicine Clinical and Translational Research
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Johns Hopkins Medicine

 
Daniel F Hanley, Jr, MD
 
The Johns Hopkins Medical Institutions
Brain Injury Outcomes Division
Texas Tech

Texas Tech University

Edward Michelson, MD FACEP
 
Department of Emergency Medicine, Paul L Foster School of Medicine
Washington U

Washington University

Rosanne Naunheim, MD
 
Barnes Jewish Medical Center
Intended patient population
Structural Injury Classifier & Brain Function Index
•  Ages 18 – 85 years, GCS 13 – 15
•  Within 72 hours of injury
Concussion Index
•  Ages 13 – 25 years, GCS 15
•  Within 72 hours of injury
 

Click here for a complete list of indications