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

这个小型企业创新研究(SBIR)第一阶段项目的更广泛的影响/商业潜力是使肌肉骨骼(MSK)损伤的超声成像得到广泛采用。在此次SBIR期间开发的由人工智能(AI)支持的超声图像分析平台可能会为更广泛的患者群体带来经济高效的诊断成像，因为超声仪器的小尺寸使该技术能够在各种环境中使用，包括医生办公室、理疗室和体育设施。大约有840,000名临床医生为MSK受伤的患者看病。这些临床医生中的每一位都代表该平台的候选客户，在美国的潜在市场总额达到17亿美元。这个小型企业创新研究(SBIR)第一阶段项目旨在推动启用人工智能(AI)的超声成像技术的发展。超声成像是当今使用的最依赖于操作者的医学成像手段，限制了超声成像在肌肉骨骼(MSK)评估中的使用。建议的方法旨在使超声能够更广泛地应用于MSK损伤的诊断。该项目涉及开发严格的数学模型，以确定帧序列是否正确对齐，并确定肩部扫描中的肌腱撕裂是否可以诊断。将开发一种引导系统，以帮助放置超声探头，从而实现对肌腱撕裂是否存在的一致识别。建议的启用人工智能的超声成像和诊断方法可能有助于从业者定位超声探头，而不需要保持精确的角度定位。一旦到达正确的起始位置，医生将根据需要使用导航系统以预先规定的运动移动探头，以准确和高效地捕获图像。该技术的目标是使新手从业者能够获取相关分析，以帮助提供诊断和治疗计划。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.