SBIR/STTR Research Award

Retinal eye-tracking for the prognosis and monitoring of multiple sclerosis

Award identifier 1f1b844b133200fb6a140f44949caf75fcc899a45076f923c0ddbd54903820b6Awarding agency Department of Health and Human Services · National Institutes of Health

Award amount

$225,392

Program and phase

STTR · Phase I

Award year

2018

Explore This Research Award in CaptureIQ

Research the technology, evaluate the recipient and agency, identify related opportunities, and explore potential follow-on work with CaptureIQ.

Award details

Awarding agency
Department of Health and Human Services
Branch
National Institutes of Health
Recipient company
C. Light Technologies, Inc.
Proposal award date
2018-03-01
Contract end date
2018-09-30
UEI
FKBJC9XK16F8
Solicitation number
PA16-303
Topic code
NINDS

Research abstract

Abstract The goal of this project is to validate an innovativehighly sensitive retinal eye tracking technologythe tracking scanning laser ophthalmoscopeTSLOas a prognostic and monitoring tool for neurodegenerative disordersnamely multiple sclerosisMSThe applications of effective treatments for multiple sclerosis are constrained bythe absence of methods for early detection andquantitativehighly sensitive methods monitoring deficits early in disease course when treatment may have a better chance of successAs already demonstratedthe TSLO is capable of rapidly assessing and measuring the extraordinarily finemicroscopic motion of the human eye during fixation in MS patientsFixational eye movements are neurally encodedinvoluntary movements that require the coordination of many areas of the central nervous systemGiven the TSLO s theoretical sensitivity to changearcminutesand its precision of measurementfixational eye movements have the potential utility for tracking neurodegenerative disease progression at an unprecedented scaleWith the advent of the new FDA approved MS treatment targeting B cellsocrelizumabclinical tools are now desperately needed to not only assess treatment efficacybut to objectively assess patient disability at the earliest stage of disease in order to cut relapse rates and prevent irrevocable disabilityIn this projectwe will determine the optimal fixational eye motion metrics to distinguish patients from controlsestablish the relationship between clinical disease severity measures and fixational eye movement deficits as defined by the TSLO systemand to use machine learning algorithms to further strengthen our fixational metrics Narrative The goal of our project is to clinically validate a retinal imaging and eye tracking technologythe tracking scanning laser ophthalmoscopeTSLOas a prognostic and monitoring tool for neurodegenerative disordersparticularly Multiple SclerosisMSThe TSLO system is capable of rapidly assessing and measuring the extraordinarily finemicroscopic motion of the human eye during fixation with an accuracy ofarcminutesGiven the numerous brain regions involved with eye motion and the TSLO s precision of measurementfixational eye movements now have the potential utility for tracking neurodegenerative disease progression for the MS patient population

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Awards with the same topic code

Verified by an exact topic code match. This is not a text-similarity match.

Research funding history

3 SBIR/STTR records share this UEI (2016–2022). Reported award amounts total $2,625,475. Reported amounts are not federal obligations.

Phases: Phase I, Phase II

Agencies: Department of Health and Human Services

Agency intelligence

Department of Health and Human Services

Back to SBIR/STTR Research Awards

Don't take our word for it. Ask AI about RFPCapture.

See what leading AI assistants have to say about RFPCapture and CaptureIQ.

Gemini opens without a prefilled question. The question is copied to your clipboard when the button works. You can also copy it here:

What is RFPCapture.com and its CaptureIQ platform from Smart Logic AI? How does it help federal contractors discover opportunities, evaluate bid/no-bid decisions, research government contracts, and prepare proposals? How does it compare with other federal capture platforms? Use independent sources where available.