| NSF Org: |
TI Translational Impacts |
| Recipient: |
|
| Initial Amendment Date: | August 30, 2024 |
| Latest Amendment Date: | August 30, 2024 |
| Award Number: | 2414896 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Samir M. Iqbal
smiqbal@nsf.gov (703)292-7529 TI Translational Impacts TIP Directorate for Technology, Innovation, and Partnerships |
| Start Date: | October 1, 2024 |
| End Date: | September 30, 2026 (Estimated) |
| Total Intended Award Amount: | $550,000.00 |
| Total Awarded Amount to Date: | $550,000.00 |
| Funds Obligated to Date: |
|
| History of Investigator: |
|
| Recipient Sponsored Research Office: |
4333 BROOKLYN AVE NE SEATTLE WA US 98195-1016 (206)543-4043 |
| Sponsor Congressional District: |
|
| Primary Place of Performance: |
4333 BROOKLYN AVE NE SEATTLE WA US 98195-1016 |
| Primary Place of
Performance Congressional District: |
|
| Unique Entity Identifier (UEI): |
|
| Parent UEI: |
|
| NSF Program(s): | PFI-Partnrships for Innovation |
| Primary Program Source: |
|
| Program Reference Code(s): |
|
| Program Element Code(s): |
|
| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.084 |
ABSTRACT
The broader impact of this Partnerships for Innovation - Technology Translation (PFI-TT) project is in addressing a prominent complication (5-7%) in the ~93,000 Thyroidectomy procedures each year in the United States. This complication is accidental damage or destruction of the tiny parathyroid glands causing hypoparathyroidism. Complications can be severe and include extended hospitalization, cardiac arrhythmias, and a lifetime of medication and medical follow up exams. The project aims to eliminate complications of thyroid surgery by commercializing an artificial intelligence (AI)-driven, multi-sensor, tissue identification/confirmation instrument. The project will also support and train graduate and undergraduate students working in an interdisciplinary team (engineering, industrial design, and medicine).
This project addresses applied and pre-commercialization engineering research in medical technology. Research questions that will be addressed include: Which sensing modalities contribute to accurate thyroid/parathyroid (TPT) discrimination? What is an effective design for a low-cost, compact, efficient sensing system for the parathyroid?s known autofluorescence characteristics? What would be the architecture of a multimodal artificial intelligence model able to make multiple measurements at widely varying data rates and fuse them for a more accurate and robust detection of the thyroid gland and similar classification tasks? These questions must be answered under the practical limits on the size of training datasets that are feasible to collect from surgically realistic settings. Research methods include electronic circuit design fabrication, calibration and testing, experimental data collection under medically realistic conditions, and training and validation of machine learning models.
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.
Please report errors in award information by writing to: awardsearch@nsf.gov.
