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| Funder | National Science Foundation (US) |
|---|---|
| Recipient Organization | University of Akron |
| Country | United States |
| Start Date | Jan 01, 2021 |
| End Date | Jun 30, 2022 |
| Duration | 545 days |
| Number of Grantees | 1 |
| Roles | Principal Investigator |
| Data Source | National Science Foundation (US) |
| Grant ID | 2049933 |
The broader impact/commercial potential of this I-Corps project is the development of a portable automated intravenous injector for easy and fast drug delivery and intravenous (IV) access in medical emergencies outside of the hospital. Each year, it is estimated that more than 350,000 people suffer from sudden cardiac arrest outside of the hospital and roughly only 10% survive.
Data show that the use of Automated External Defibrillators (AEDs) save more than 1600 people each year. The proposed portable automated intravenous injector would complement existing AEDs, which are widespread and commonly available. It is estimated that in emergency situations roughly 20% more lives could be saved if epinephrine or other drugs are used in conjunction with an AED.
The proposed device may allow a non-trained operator to inject rescue medication following the American Heart Association (AHA) guidelines, by using a proprietary learning algorithm that will not require extensive medical knowledge. It is estimated that the market size is around $5B for such a device that is complementary to the AED market. The global market size for intravenous access is estimated to about $11B including adjacent markets in hospital blood sampling, drug injection and other applications.
This I-Corps project is based on the development of a portable automated intravenous injector, which is a smart injection device that senses the location and depth of a vein and injects a preset and safe dosage of a drug (in this application, epinephrine) using an algorithm derived from a combination of machine learning and big data. When attached to the forearm, the proposed technology is able to find the location and depth of the vein for drug injection automatically.
It has two degrees of freedom. One is to move along the width of the forearm, and the second is to insert the needle inside the vein. The proposed technology uses ultrasound along with infrared light to locate and find the depth of the vein.
It leverages Artificial intelligence algorithms to accurately detect the location of the vein and evaluate the patient's emergency. The system is designed to continue to learn through the usage and trial phase, and as data continues to build the system it continues to get “smarter” and more accurate over time. The historical data may be mined further to allow for smarter insights into cardiac care decisions.
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.
University of Akron
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