Machine Learning

Sparta Science Awarded USAF SBIR Phase I Contract

Company is Actively Collaborating with DOD on Multiple SBIR Phase I and Phase II projects

Sparta Science, the developer of a scalable, machine learning-based movement health platform, was recently awarded an AFWERX Small Business Innovation Research (SBIR) Phase I contract by the United States Air Force (USAF) to develop an objective, scalable decision support tool to aide in musculoskeletal injury (MSKI) return-to-duty (RTD) determinations. This award is significant because it will allow Airmen and their care teams to benefit from the greater standardization built into this decision-support metric, ensuring they are released to full unrestricted duty at the optimal time.

Sparta Science is actively collaborating with the Department of Defense (DOD) on multiple SBIR Phase I and Phase II projects to improve warfighter readiness by modifying Sparta Science’s commercially successful movement health intelligence platform to address various Department of the Air Force (DAF) use cases. These initiatives will explore opportunities to enhance clinicians’ and practitioners’ abilities to deliver effective movement health guidance at a scale relevant to the USAF.

Under this award, Sparta Science will explore R&D partnerships with training units that experience a high volume of MSKIs, as well as transition and acquisition strategies with program offices to ensure the successful enterprise-wide implementation of Sparta’s RTD decision support tool.

“Musculoskeletal injury is the number one reason for medical non-deployability across the Air Force,” said Dr. Phil Wagner MD, CEO and Founder of Sparta Science. “This project will allow Sparta Science to develop software to increase rehabilitation professionals’ confidence and effectiveness in RTD decisions and to become a component of the RTD decision process across the DOD.”

The Air Force is deliberately investing in reducing the immense burden of MSKI to ensure full force readiness for worldwide deployability. This project will develop evidence-based support for RTD decision-making to determine when RTD is safe and appropriate, optimizing the readiness of the force.

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