| NSF Org: |
TI Translational Impacts |
| Recipient: |
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| Initial Amendment Date: | August 26, 2021 |
| Latest Amendment Date: | August 26, 2021 |
| Award Number: | 2143515 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Ruth Shuman
rshuman@nsf.gov (703)292-2160 TI Translational Impacts TIP Directorate for Technology, Innovation, and Partnerships |
| Start Date: | September 1, 2021 |
| End Date: | August 31, 2023 (Estimated) |
| Total Intended Award Amount: | $50,000.00 |
| Total Awarded Amount to Date: | $50,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
11200 SW 8TH ST MIAMI FL US 33199-2516 (305)348-2494 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
11200 SW 8TH ST Miami FL US 33199-0001 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | I-Corps |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.084 |
ABSTRACT
The broader impact of this I-Corps project is the development of automated diagnosis tools for neurological disorders. Currently, there is no single quantitative test (like a blood glucose test) that can be done to diagnose neurological disorders. The proposed technology is based on advanced deep learning models applied to multi-modal imaging, such as magnetic resonance imaging (MRI) and electroencephalography (EEG), for early detection. The technology may be used to distinguish abnormal brain scans from healthy scans and may be able to aid in the diagnosis of neurological disorders such as autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), Alzheimer?s disease (AD), epilepsy, and Parkinson?s disease (PD). For example, current ADHD diagnostic tests result in the mis-, over-, or under-diagnosis of nearly a million children annually. If successful, the proposed technology may lead to enhanced diagnostic accuracy and improved clinical outcomes.
This I-Corps project is based on the development of advanced machine learning algorithms including deep learning (DL), network science principles, and data-driven approaches that may identify markers for neurological disorders. The goal is to provide a diagnostic test that is non-invasive, represents progression of the neurological disorder, correlates with symptomatology, and provides early-detection. A single quantitative test does not exist for most neurological disorders or relies on single-modality data for disorders such as epilepsy. Preliminary data suggests that applying DL methods to multiple modalities of brain data (functional MRI - fMRI, structural MRI - sMRI, EEG) will lead to a transformative artificial intelligence (AI)-based, quantitative strategy to diagnose these disorders ? without the limitation of the current clinical methods. Successful models for the diagnosis of ASD and ADHD have been designed and results indicate an accuracy improvement of up to 28% using MRI data alone. The technology for early detection of epilepsy and AD are currently under development. These models will serve as proof-of-concept and a base for developing solutions for multi-disorder diagnosis and prediction using multi-modal imaging data.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Currently, there is no single quantitative test that can be done to diagnose neurological disorders such as Autism Spectrum, ADHD, or Alzheimer’s. The diagnostic processes are mostly based on symptomology adhering to the Diagnostic and Statistical Manual (DSM) symptom-based criteria where informants observe the subjects across several types of day-to-day environments such as in a school or at home. Unfortunately, these tests are subjective, can introduce biases against certain communities, and may increase the mis-diagnosis rate. In contrast, though quantitative tests do exist for disorders such as epilepsy in general, there are very few quantitative solutions for early oncoming seizure warning available to first responders. The predictive or diagnostic process is length, cumbersome, and require extensive resource and expertise to be administered correctly. These questions were part of our research agenda, and we wanted to know if there was a sufficient interest and product-market fit to float these diagnostic and predictive models for public health.
As a result of this National ICorp, we pivoted our shift in focus from mental disorders to the more general category of neurological disorders especially, the development of models and systems for epileptic seizure prediction. To this end, we conducted more than 150 interviews for various categories of professionals and patients including Psychiatrists, Psychologists, Epileptologist, First responders, insurance, medical liaisons, FDA and more importantly patients and their caregivers. In total, we started with 40 hypotheses across the various sections of the business model canvas (BMC). In total, we tested 34 hypotheses (85%); we were able to validate 23 (about 68%) and invalidated the remaining 11. These interviews and data were collected over the phone, zoom or by attending various trade shows related to psychiatry or neurology.
We are highly appreciative of this National ICorp which result in polishing our product-market fit that was admired by patients and their care givers alike. As a result of the data and training that we got from this ICorp we were able to accomplish the following:
1) We were awarded a NSF PFI grant that proposes to design and develop machine-learning models for epileptic seizure prediction. Note that this specific category of neural disorder was acquired directly as a result of this ICorp training and collected data.
2) We trained multiple PhD students, and postdocs as well as other trainees to look for product-market fit and how these can be used to make a positive difference in the world.
3) As a result of this grant, Dr. Saeed successfully founded a startup company AI-NeoTech LLC that will develop and commercialize AI based solutions to various mental disorders especially for epilepsy, Autism and Alzheimer’s.
4) As a result of this Icorp training and data, we submitted a NSF SBIR phase I grant which was recently award to us as a NSF STTR phase I. We look forward to developing this technology that can help more than 3 million patients, and their caregivers in the US – significantly improving their quality of life.
The commercial impact of the proposed technology (in the STTR phase I award that came directly using the data from this NSF Icorp) includes a technological advance towards the first ever wearable seizure prediction device in the US market, taking the digital health market forward in the field of neurology and epileptology and hopefully, the creation of 100’s of American jobs.
Last Modified: 11/13/2023
Modified by: Fahad Saeed
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