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Neuropsychological digital assessment test that identifies discrete latencies in response time to identify emergent neurological/nueropsychiatric illness

Technology

Technology

Declining cognitive health is increasing in the U.S and worldwide. Early detection of impairment is crucial to altering the course of disease. Many cognitive assessments have previously focused on early memory defects, such as those associated with Alzheimer’s, and it is now evident that focused deficits accumulate over several decades before memory symptoms appear. Although the development of fluid biomarkers, positron emission tomography (PET) ligands, and volumetric magnetic resonance imaging (MRI) have enabled the molecular, functional, and structural staging of disease progression, clinical focus is shifting to the pre-symptomatic stages. Therefore, it is necessary to reliably and quantitatively assess cognitive domains that are subtly affected before memory for early therapeutic intervention. Digital technologies offer the promise of personalized remote monitoring of medical conditions and the conversion of subjective assessments into more scalable and objective measurements. Combined use of digital administration and scoring allows for the identification of points where test scores are accurate, but total time and/or discrete latencies are slow, allowing for identification of emergent illness such as Alzheimer’s disease, given that slow yet accurate responding indicates difficulty in recruiting neurocognitive resources. A protocol of six separate commonly used tests are incorporated with digital assessment to identify neuropsychological impairment.

 

Competitive Advantage

  • Combines the accuracy of test completion with total time and discrete latency in response to assess risk for emergent and subtle neurological disease
  • Enables conversion of subjective assessments into scalable objective measurements for greater accuracy and reliability
  • Highly sensitive for prediction of subtle changes, predictive, requires minimal specialized training, non-invasive, easy to administer/low-cost
  • Can be licensed for clinical use and fulfill existing and new/upcoming Medicare regulations for neurocognitive screening during an annual wellness visit

 

Opportunity

Currently, in the United States alone, 5.3 million people are diagnosed with Alzheimer’s, with 13.8 million projected to be diagnosed by 2050. The World Health Organization (WHO) estimates that 47.5 million people have dementia worldwide and the total number of people with dementia is projected to reach 75.6 million in 2030 and almost triple by 2050 to 135.5 million. The global cognitive assessment and training healthcare market is expected to grow from $1.98 billion in 2016 to $8.06 billion in 2021at a CAGR of 32.3%. Major factors contributing to market growth include an aging global population, increasing awareness for brain fitness, and technological advancements. These factors are expected to drive the adoption rate of cognitive assessment and training in the coming years. Along with solutions to monitor changes in cognitive function, use of assessments in clinical trials is expected to grow at the highest CAGR during the forecast period, as cognitive assessment solutions are extensively used to improve research and development. Rowan University is looking for a partner for further development and commercialization of this technology through a license.

Patent Information:
Title App Type Country Serial No. Patent No. File Date Issued Date Expire Date Patent Status
Method And Apparatus For Measuring Cognitive Vital Sign Using Latencies And Time To Completion Obtained From Neuropsychological Tests US Provisional United States 62/637,172   3/1/2018     Filed
Automated Detection of Cognitive Conditions Patent Cooperation Treaty United States PCT/US2019/020365   3/1/2019     Published
For Information, Contact:
Yatin Karpe
Director
Rowan University
karpe@rowan.edu
Inventors:
David Libon
Ganesh Baliga
Mary Kerwin
Rod Swenson
Keywords:
Alzheimer's
Assesments
Cognition
Dementia
Intervention
Neurodegeneration
Quantitative