Abstract & Details
Description
Award ID: 2121531
The broader impact/commercial potential of this I-Corps project is the introduction of a point-of-care ultrasound device that may reduce the cost of data acquisition in research and support scientific advancements in patient care. Currently, over 60% of the world lacks access to ultrasound imaging technology. In addition to prohibitive cost, training also is a barrier. With the implementation of machine learning algorithms, an aspect of guidance and image classification is introduced in the proposed device. This may facilitate the screening of potential complications during pregnancy and provide guidance during breast biopsies, among other applications. To train machine learning algorithms, large datasets need to be generated, but ultrasound data is expensive and slow to collect. In the United States, more than 125 million ultrasound studies are performed yearly at an average cost of $800 each. The main advantages of a broadly accessible ultrasound device are global accessibility to affordable medical diagnostic technology, and a platform for telemedicine software applications. Sonograms may then be shared wirelessly with physicians and healthcare providers, potentially eliminating the need for a physical office visit, which for many is a long, expensive, and complicated journey. This proposed point-of-care ultrasound system will address the needs of global users. This I-Corps project is based on the development of a hand-held, USB (universal serial bus)-powered device that generates 2-dimensional and 3-dimensional ultrasound sonograms and streams images in the same format as webcam video. This video may then be displayed or processed by diagnostic software. The hardware and software are user friendly and applicable for a range of audiences. In addition, artificial intelligence can be used to reduce the training burden for new users. The axial resolution of the proposed portable system is 0.6 mm, lateral resolution is 1.9 mm at 30 mm depth, and central frequency is 4.25 MHz 5%. When tested with tissue equivalent phantoms, the device rendered images of sufficient quality to extract value and critical information such as the size of internal structures as well as volumetric information, which may be used to guide clinical decisions. Cart-based ultrasound systems that provide the best-in-market 3-dimensional image quality and processing software can retail for $250,000, which is prohibitive in price for many markets. The proposed point-of-care system may eliminate barriers to diagnostic imaging including price, lack of proficiency in sonogram interpretation, and difficulty reaching medical facilities. The combination of performance, simplicity, and price make this technology broadly accessible and manageable for a range of applications. 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.
NSF Program Director: Ruth Shuman
The broader impact/commercial potential of this I-Corps project is the introduction of a point-of-care ultrasound device that may reduce the cost of data acquisition in research and support scientific advancements in patient care. Currently, over 60% of the world lacks access to ultrasound imaging technology. In addition to prohibitive cost, training also is a barrier. With the implementation of machine learning algorithms, an aspect of guidance and image classification is introduced in the proposed device. This may facilitate the screening of potential complications during pregnancy and provide guidance during breast biopsies, among other applications. To train machine learning algorithms, large datasets need to be generated, but ultrasound data is expensive and slow to collect. In the United States, more than 125 million ultrasound studies are performed yearly at an average cost of $800 each. The main advantages of a broadly accessible ultrasound device are global accessibility to affordable medical diagnostic technology, and a platform for telemedicine software applications. Sonograms may then be shared wirelessly with physicians and healthcare providers, potentially eliminating the need for a physical office visit, which for many is a long, expensive, and complicated journey. This proposed point-of-care ultrasound system will address the needs of global users. This I-Corps project is based on the development of a hand-held, USB (universal serial bus)-powered device that generates 2-dimensional and 3-dimensional ultrasound sonograms and streams images in the same format as webcam video. This video may then be displayed or processed by diagnostic software. The hardware and software are user friendly and applicable for a range of audiences. In addition, artificial intelligence can be used to reduce the training burden for new users. The axial resolution of the proposed portable system is 0.6 mm, lateral resolution is 1.9 mm at 30 mm depth, and central frequency is 4.25 MHz 5%. When tested with tissue equivalent phantoms, the device rendered images of sufficient quality to extract value and critical information such as the size of internal structures as well as volumetric information, which may be used to guide clinical decisions. Cart-based ultrasound systems that provide the best-in-market 3-dimensional image quality and processing software can retail for $250,000, which is prohibitive in price for many markets. The proposed point-of-care system may eliminate barriers to diagnostic imaging including price, lack of proficiency in sonogram interpretation, and difficulty reaching medical facilities. The combination of performance, simplicity, and price make this technology broadly accessible and manageable for a range of applications. 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.
NSF Program Director: Ruth Shuman
| Status | Closed |
|---|---|
| Effective start/end date | 03/01/21 → 12/31/22 |
Funding
- I-Corps Teams: $50,000.00
Active Fiscal Year
- FY2023
- FY2022
Start Fiscal Year
- FY2021
TIP Programs
- I-Corps Teams
Key Technology Areas
- Artificial Intelligence
- (confidence score: 100%)
- Biotechnology
- (confidence score: 100%)
Technology Foci
- Medical Technology
- (confidence score: 100%)
- Artificial Intelligence (Broad)
- (confidence score: 100%)
Congressional District at Award
- District n. 13 of New York
Current Congressional District
- District n. 13 of New York
United States
- New York
Core Based Statistical Area (CBSA)
- New York-Newark-Jersey City, NY-NJ
County
- County: New York, NY
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