SBIR Phase II: Compact, Low-cost, Automated 3D Ultrasound System for Regular and Accessible Breast Imaging

The broader/commercial impact of this SBIR Phase II project introduces a new paradigm in breast cancer screening with a cost-effective and accessible platform for personalized breast health monitoring, empowering women and their physicians with accurate and actionable data. In the US, over 300,000 women are diagnosed and 40,000 women die from breast cancer annually. Breast cancer has a 99% survival rate if detected early, but limitations in cost, sensitivity, and accessibility of current screening result in missing 1 in 3 cancers at early stages. Early detection is associated with lower costs of treatment that save billions of dollars in direct medical care and lost productivity annually, demonstrating a clear economic and societal benefit for better breast cancer screening platforms. The technology leverages the proven benefits of automated ultrasound and the newfound power of cloud-based artificial intelligence to expand the deployment of these systems, including lower-resource settings such as walk-in or rural clinics, pharmacies, and in the home for self-monitoring. The platform's portability, low cost, 2-minute scan time, automated analysis, and patient-centered design greatly increases the accessibility and adoption of breast cancer screening, resulting in better clinical outcomes and a reduced cost burden to the US healthcare system. This SBIR Phase II project proposes to continue development of a novel platform that combines 3D automated ultrasound with artificial intelligence (AI) for personalized and accessible breast imaging. The proposed project will improve the performance of a compact scanner and wearable accessory combination to produce repeatable images independent of operator training; this can be accomplished in under 2 minutes without expensive capital equipment, ionizing radiation, or patient discomfort. The intuitive software will enable physicians to visualize whole breast volume and accurately localize and measure lesions. AI will identify abnormal masses and predict the probability of malignancy to help physicians with accurate and fast diagnosis. The Phase II R&D focuses on five objectives: (i) optimize system performance for high-quality whole breast imaging with a new beamforming technique for higher resolution, with higher frame rates and faster scan; (ii) finalize the wearable and scanner design to ensure reliable operation with water as the coupling medium; (iii) conduct usability verification and validation regarding safety and functional requirements for clinical use; (iv) conduct a small study to verify the scanner's ability in finding existing breast lesions; (v) develop a machine learning engine for real-time detection and characterization of lesions in images acquired with the ultrasound scanner. 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.