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Computer-aided detection of focal cortical dysplasias

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 2R44NS083101-02A1
Agency Tracking Number: R44NS083101
Amount: $1,501,354.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: 108
Solicitation Number: PA14-072
Timeline
Solicitation Year: 2015
Award Year: 2015
Award Start Date (Proposal Award Date): 2015-05-01
Award End Date (Contract End Date): 2018-04-30
Small Business Information
100 CAPTAINS ROW, APT. 108
Chelsea, MA 02150-4021
United States
DUNS: 078509164
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 NICHOLAS SCHMANSKY
 (617) 959-1896
 nicks@corticometrics.com
Business Contact
 NICHOLAS SCHMANSKY
Phone: (617) 959-1896
Email: nicks@corticometrics.com
Research Institution
N/A
Abstract

DESCRIPTION provided by applicant This project proposes to build computer aided detection CAD software for use in identifying cortical malformations known as focal cortical dysplasiaandapos s FCDs which are a common cause of epileptic seizures The intent is for the software used by a neuroradiologist at a clinical workstation to decrease the time intensive nature of the visual search for cortical dysplasiaandapos s while simultaneously increasing sensitivity o dysplasia identification thus reducing the number of missed lesions and making neuroradiologists more effective and more efficient Epilepsy is a common neurological disorder characterized by recurrent unprovoked seizures that exacts a large toll upon society in terms of both quality of life and health care costs Malformations of cortical development MCD are the most common cause of seizures in children and the second most common cause in adults Focal cortical dysplasia is a common form of MCD that is responsible for the vast majority of treatment resistant epilepsy in patients with MCD and when anti epileptic medication is ineffective detection of FCD becomes critical to the ability of the epilepsy team to offer surgery which is often these patientandapos s last hope for seizure freedom Unfortunately the radiological diagnosis of FCD is exceedingly difficult in a large percentage of cases due to their focal and subtle nature Thus while resection of these dysplasias can often cure seizures they can be missed for years or decades resulting in increased neurological damage and degradation of quality of life due to chronic seizures In principle high resolution MRI can be used to increase diagnostic accuracy While this is becoming more common in clinical practice the need for high patient throughput lack of clinical information and inexperience often results i these lesions being missed on routine clinical reads by neuroradiologists The project will build upon a foundation of existing technology for the generation of quantitative measures of the human brain based on MRI imaging known in the neuroimaging research domain as FreeSurfer The project will make use of an MRI dataset of subjects with histologically confirmed FCD to be labeled by four neuroradiologists and control subjects with epilepsy that is not due to FCD The project has three aims gathering the dataset and expansion of the detection algorithms tested in Phase I to include additional MRI biomarkers development of an MRI scanner slice prescription component to ensure imaging of an FCD at the optimal visualization plane and an aim to submit a commercialized version of FreeSurfer for FDA k clearance The latter aim is important for the long term project goal of advancing the state of other clinical detection methods through the building of additional CAD tools making use of FreeSurferandapos s brain measures including diseases as varied as Huntingtonandapos s disease Alzheimerandapos s disease tumor monitoring and hydrocephalus PUBLIC HEALTH RELEVANCE The proposal is to build software for computer aided detection of focal cortical dysplasias FCDs a malformation of brain development that is a common cause of epileptic seizures in children and adults The proposed software will offer a neuroradiologist a faster and more accurate detection method compared to visual inspection only By identifying abnormalities otherwise missed the ensuing surgical removal of an abnormality can often stop seizures

* Information listed above is at the time of submission. *

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