Spark Neuro Inc

Improving an Eeg-based Neurodiagnostic Software Platform to Detect Alzheimer's Disease in Mci Patients - Project Summary Alzheimer’s Disease (AD) Is a Progressive, Neurodegenerative Condition and the Most Common Cause of Dementia. in the United States, an Estimated 6.2 Million People Over the Age of 65 Are Living With Ad, 72% of Whom Are Over 75 Years Old. Given the Country’s Aging Population, This Number Is Expected to More Than Triple by 2050, Costing the United States an Annual $600 Billion in Associated Healthcare Costs. Early Diagnosis Is Crucial to Ad Treatment Because It Allows Clinicians More Time to Find and Initiate Treatment Pathways, Which Decreases Disease Progression and Preserves Mental Capacity. New Research Suggests That Biomarkers Can Help Diagnose Ad Years Before Symptoms Appear. Despite Recent Technological Advancements, Many Tools and Technologies That Measure Biomarkers Are Invasive, Expensive, and Not Sensitive or Specific Enough, Particularly When Detecting the Disease at Earlier Stages, Limiting Their Usability. When Combined With Advanced Machine Learning Techniques, Electroencephalography (EEG) Has Been Shown to Address Many of the Existing Issues Related to Ad Biomarkers. at Spark Neuro, We Aim to Unlock the Full Potential of Eeg Through a Novel Software Platform. Combining Eeg With the Capabilities of Machine Learning, Our Model Better Assesses Cognitive Health and Neurodegeneration, Aiding the Diagnosis of Ad. Spark’s Neuroanalytic Platform Will Be a Standardized, Objective, Non-invasive, Cost-effective Diagnostic Tool Capable of Highly Sensitive and Specific Detection of Cognitive Impairment Across the Entire Disease Continuum. Our Platform Would Vastly Expand Ad Screening Initiatives and Provide Neurological Insights to Aid in the Diagnosis and Tracking of Disease Progression. During the Proposed Phase I Research, We Will Work in Collaboration With Mayo Clinic to Extend Our Current Algorithm to Assess and Differentiate Patients in the Earlier, Mild Cognitive Impairment Stage of the Disease, and Provide Highly Useful and Usable Reports to Clinicians. First, We Will Optimize the Algorithm by Incorporating Eeg Data Collected From Mayo Clinic Patients. Next, We Will Focus on Improving the User Experience of Both Eeg Data Acquisition and Clinical Reporting. We Will Enhance End-user Satisfaction and Optimize the Technology to Fit Within Current Clinical Workflows. Participating Mayo Clinic Eeg Technicians Will Provide Feedback. Once Optimized, Spark’s Approach Will Constitute the First In-office Eeg-based Neurodiagnostic Tool Specifically for Diagnosing and Tracking Ad. Our Non-invasive Solution Has the Potential to Accelerate Ad Screening Programs, Detect Pathological Ad at Earlier Stages, and Provide Individualized Disease Progression Insights.