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

该SBIR第二阶段项目的更广泛/商业影响为乳腺癌筛查引入了一种新的模式，具有成本效益和可访问的个性化乳腺健康监测平台，为妇女及其医生提供准确和可操作的数据。在美国，每年有超过300，000名妇女被诊断出患有乳腺癌，40，000名妇女死于乳腺癌。如果早期发现，乳腺癌的存活率为99%，但目前筛查的成本，灵敏度和可及性的限制导致早期阶段三分之一的癌症缺失。早期检测与较低的治疗成本有关，每年可节省数十亿美元的直接医疗费用和生产力损失，这表明更好的乳腺癌筛查平台具有明显的经济和社会效益。该技术利用自动化超声的成熟优势和基于云的人工智能的新功能来扩展这些系统的部署，包括低资源环境，如步入式或农村诊所，药店和家庭自我监测。该平台的便携性，低成本，2分钟扫描时间，自动分析和以患者为中心的设计大大提高了乳腺癌筛查的可及性和采用率，从而获得更好的临床结果并降低了美国医疗保健系统的成本负担。该SBIR第二阶段项目建议继续开发一种新型平台，将3D自动超声与人工智能（AI）相结合，用于个性化和可访问的乳腺成像。拟议项目将提高紧凑型扫描仪和可穿戴配件组合的性能，以产生独立于操作员培训的可重复图像;这可以在2分钟内完成，无需昂贵的资本设备、电离辐射或患者不适。直观的软件将使医生能够可视化整个乳房体积，并准确定位和测量病变。AI将识别异常肿块并预测恶性肿瘤的可能性，以帮助医生进行准确和快速的诊断。第二阶段的研发&重点是五个目标：（i）优化系统性能，采用新的波束成形技术实现高质量的全乳房成像，以实现更高的分辨率、更高的帧速率和更快的扫描;（ii）完成可穿戴设备和扫描仪的设计，以确保以水作为耦合介质的可靠操作;（iii）就临床使用的安全性和功能要求进行可用性验证和确认;（iv）进行一项小型研究，以核实扫描器能否发现现有的乳房病变;（v）为真实的开发机器学习引擎-该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的学术价值和更广泛的影响审查标准。

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.