SBIR Phase I: Artificial intelligence (AI)-enabled ultrasound for imaging and diagnosing musculoskeletal injuries

Award ID: 2212911

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to enable widespread adoption of ultrasound imaging for musculoskeletal (MSK) injuries. The ultrasound image analysis platform enabled by artificial intelligence (AI) developed during this SBIR may bring cost-effective diagnostic imaging to a broader patient population as the small form factor of the ultrasound instrument enables the technology to be used in a variety of settings, including doctors offices, physical therapy offices, and sports facilities. There are approximately 840,000 clinicians who see patients with MSK injuries. Each of these clinicians represents candidate customers for the platform, leading to a total addressable market in the U.S. of $1.7 billion. This Small Business Innovation Research (SBIR) Phase I project seeks to advance artificial intelligence (AI)-enabled ultrasound imaging technology. Sonography is the most operator-dependent medical imaging modality in use today, limiting the use of ultrasound imaging in musculoskeletal (MSK) evaluation. The proposed approach seeks to enable the wider adoption of ultrasound for diagnosing MSK injuries. This project involves the development of rigorous mathematical models to determine whether a frame sequence is properly aligned and determine if tendon tears in shoulder scans are diagnosable. A guidance system will be developed to aid in ultrasound probe placement so that consistent identification of tendon tear presence or absence is achieved. The proposed AI-enabled approach to ultrasound imaging and diagnostics may help practitioners position the ultrasound probe without the need to maintain precise angular positioning. Once at the correct starting position, the practitioner will move the probe in a pre-prescribed motion using a guidance system as needed for accurate and efficient image capture. The goal of the technology is to enable novice practitioners to capture relevant analyses to assist in providing a diagnosis and a treatment plan. 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: Alastair Monk