SBIR-STTR Award

This Small Business Innovation Research Phase I project will develop computer software that automatically analyzes medical images to diagnose brain diseases affecting the elderly (such as Alzheimer's disease) that cause difficulty with memory, thinking, judgment, and other normal functions. Such diseases affecting the elderly share similar symptoms, so they can be very difficult to diagnose and it is even more difficult to predict whether the patient will worsen. The software we will develop will use a patient's brain scan to determine which disease the patient actually has, and if the disease is Alzheimer's, the software will determine the stage of progression of the disease, and will aim to predict future decline. The broader/commercial impacts of this research are numerous. This project will create new jobs by launching a new business that will lead to the first product of its kind. The market for the proposed product is very large, and the applicant company is well placed for commercialization, as its parent companies are already in the business of providing brain-image analysis for the pharmaceutical industry, and have already been providing preliminary diagnostic reports on patients for select clinicians on a research basis. During the project, input and feedback will be sought from a major drug company, a major imaging manufacturer, and leading physicians, to ensure that the product meets real-world needs of patient and health-care providers, and carefully considers critical issues for commercialization

This Small Business Innovation Research (SBIR) Phase II project addresses a critical need for accurate patient diagnosis in the development of effective therapeutics for Alzheimer's Disease and other dementias, and in patient treatment. The high variability of patients selected for dementia clinical trials, including misdiagnosis, has been a significant contributor to the high failure rates of these trials. The diagnostic under development in this grant would provide an important way to reduce misdiagnosis and patient variability. A main objective of Phase II is to expand the glucose metabolism dementia diagnostic developed in Phase I to be capable of analyzing amyloid PET images and structural magnetic resonance images (MRI), and to combine data from one or more of these imaging modalities into an optimal diagnostic and prognostic assessment. The research will include the development, refinement and testing of: single-modality analysis methods that identify the type of dementia that a patient has, single-modality analysis methods that place the patient along a scale of disease severity, methods that combine and optimize information from multiple imaging modalities, and automated software to conduct these analyses. In addition, reports will be further developed and refined with feedback from physicians and pharmaceutical partners to clearly communicate results. The broader impact/commercial potential of this project is the ability to address a major barrier of patient variability in achieving successful clinical trials, thus increasing the opportunity to find effective therapeutics for Alzheimer's Disease and other dementias. More than 6 million persons in the U.S. alone are affected by these dementias, with a societal cost estimated at more than $400 billion. This population is rapidly growing as the population ages, and the need to effectively identify and treat patients is urgent. The use of the technology to screen patients for clinical trials and stratify them for analysis is expected to be of immense value to pharmaceutical companies and is an initial commercial opportunity. Already, the developments of Phase I are in use for projects with two such companies. The ability to accurately diagnose patients will help to address the need to correctly treat these patients. The project will result in job creation as the initial service model and subsequent product model are implemented. Finally, by allowing characterization of the subjects in clinical trials, this project will aid in the scientific understanding of the progression of disease and its relationship to symptoms and response to therapy