SBIR Phase II: Guided Positioning System for Ultrasound

这个小企业创新研究（SBIR）第二阶段项目的更广泛影响/商业潜力将在医疗保健领域。美国每年每人在医疗保健上的支出约为9000美元。超声医学成像是一种医学成像技术，可以通过提供一种替代高成本成像技术来降低成本。该二期项目创造的技术有望提高医疗超声的质量、价值和可及性，从而降低美国医疗保健系统的医疗成像成本。此外，该公司的技术有望将超声波带到更多的临床环境中，并提高疾病诊断和治疗的全系统效率。这项技术在国际市场上也有商业潜力，全球每年在医疗超声设备上的花费为58亿美元。最后，通过提高超声波的效用，这项技术将改善病人的护理，并可能最终挽救生命。这个小企业创新研究（SBIR）二期项目将开发用于医学超声成像的深度学习技术。超声成像有许多优点，包括实时图像采集、非侵入性扫描、低成本设备和无已知副作用（它是非电离的）。然而，质量的可变性阻碍了它的采用和使用。因此，通常使用更昂贵的成像，经常使患者暴露在电离辐射下。我们的目标是开发、改进和测试基于深度学习的机器学习技术，以改善超声采集和解释。我们期望这个项目将创造出新的技术，使超声更容易使用，并提高超声检查的质量。最终结果将提高医学超声检查的质量、价值和可及性，将节省医疗保健系统的成本，将改善患者护理，并将支持可持续的商业机会。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project will be in the field of healthcare. The United States spends approximately $9,000 per person per year on healthcare. Ultrasound medical imaging is a medical imaging technology that could lower costs by providing an alternative to higher-cost imaging techniques. The technology created during this Phase II project is expected to increase the quality, value, and accessibility of medical ultrasound, which would in turn reduce medical imaging costs in the US healthcare system. Furthermore, the company's technology is expected to bring ultrasound to more clinical settings and improve system-wide efficiencies in the diagnosis and treatment of disease. The technology also has commercial potential in the international market, with $5.8B spent annually on medical ultrasound devices worldwide. Finally, by improving the utility of ultrasound, the technology will lead to improved patient care and may ultimately save lives.This Small Business Innovation Research (SBIR) Phase II project will develop deep learning technology for ultrasound imaging in medicine. Ultrasound imaging has numerous benefits including real-time image acquisition, non-invasive scanning, low-cost devices, and no known side-effects (it is non-ionizing). However, variability in quality has encumbered its adoption and utility. As a result, more expensive imaging is typically utilized, often exposing patients to ionizing radiation. Our objective is to develop, improve, and test machine learning techniques, based on deep learning, to improve ultrasound acquisition and interpretation. We expect this project will create novel technologies that make ultrasound easier to use and improve the quality of ultrasound examinations. The end result will improve the quality, value, and accessibility of medical ultrasound examinations, will result in cost savings to the healthcare system, will produce improvements in patient care, and will support a sustainable business opportunity.