Automatic, Opportunistic Surveillance of Hip Bone Fragility in X-ray Images

X 射线图像中髋骨脆性的自动、机会性监视

• 批准号: 10697573
• 负责人: ARA NAZARIAN
• 金额: $ 91.59万
• 依托单位: BIOSENSICS, LLC
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10697573
• 关键词: Academic Medical Centers; Age; Alcohol consumption; Algorithms; American Medical Association; Architecture; Area; Artificial Intelligence; Biomechanics; Body mass index; Bone Density; Caring; Categories; Classification Scheme; Clinical; Clinics and Hospitals; Cloud Service; Code; Collaborations; Computer software; Country; Current Procedural Terminology; Current Procedural Terminology Codes; Data; Databases; Detection; Developed Countries; Development; Diagnostic; Disease; Dual-Energy X-Ray Absorptiometry; Endocrinology; Ethnic Origin; Evaluation; Face; Femoral Fractures; Femur; Fracture; Geometry; Glucocorticoids; Health care facility; Hip Fractures; Hip region structure; Hospitals; Image; Imaging Device; Incidence; Income; Individual; International; Intraobserver Variability; Ionizing radiation; Life Cycle Stages; Life Expectancy; Machine Learning; Maintenance; Marketing; Measurement; Measures; Medical Device; Medical Imaging; Metastatic Neoplasm to the Bone; Morocco; Orthopedics; Osteoporosis; Outcome; Parents; Patient Care; Patients; Persons; Phase; Pilot Projects; Preventive measure; Probability; Procedures; Process; Radiation; Radiation Dose Unit; Radiology Specialty; Recording of previous events; Reporting; Research; Resolution; Rheumatoid Arthritis; Risk; Roentgen Rays; Rotation; Secure; Small Business Innovation Research Grant; Smoking; Specimen; Spinal Fractures; Structure; System; Technology; Time; Tube; Validation; Vertebral Bone; Visualization; Weight-Bearing state; Woman; X-Ray Computed Tomography; X-Ray Medical Imaging; automated analysis; bone; bone fragility; bone geometry; bone mass; cloud based; cost; density; design; editorial; fracture risk; fragility fracture; high risk; hip bone; image processing; imaging study; indexing; insight; machine learning classification; medical schools; men; mortality; radiological imaging; screening; sex; soft tissue; standard measure; tool; tumor; voltage

PROJECT SUMMARY / ABSTRACT Approximately 1 in 3 women and 1 in 5 men over the age of 50 will suffer from a fragility fracture in their remaining lifetime. Fragility hip fracture is one of the most serious and debilitating outcomes of osteoporosis with a 20–40% mortality rate during the first year after the fracture. Hip fracture incidence rates are known to increase exponentially with age in both women and men; and with the rising life expectancy throughout the globe, the number of men and women who will be above the threshold of fragility fracture is expected to almost double, with a prediction of 319 million cases by 2040. Thus, the number of fractures is predicted to double as well. In this Direct Phase II SBIR, BioSensics, in collaboration with orthopaedic, radiology, endocrinology and biomechanics experts at Harvard Medical School, proposes to develop a cloud-based software solution for automatic, opportunistic screening for hip fracture risk using plain X-ray images, called XFx. X-ray studies are ubiquitous in all corners of the world, are inexpensive and provide high resolution studies that offer insight into bone geometry, microstructure and density, at a low ionizing radiation dose. The proposed software solution will include 1) a desktop application for uploading X-ray images and displaying and visualizing XFx results, and 2) a secure cloud-based backend for receiving the uploaded X-ray images and performing the analysis. The software architecture will support on-premise integration with a hospital cloud services (e.g., PACS systems) to enable automatic, opportunistic screening for hip fracture risk using plain X-ray images. The solution will stand by in the central imaging data server of hospitals or clinics, investigate each non-investigated X-ray image, and if recognized to include a proximal femur, automatically execute the AI/ML-based classification scheme to identify patients with osteoporosis or at high risk of hip fracture. If a patient is identified to have osteoporosis or a high risk of fracture, the software will flag the patient. The clinician providing care for the patient will then be prompted to consider ordering an evaluation of fragility fracture risk and receive a full report. This process is reimbursable under the Current Procedural Terminology (CPT) code 76499 “Unlisted Diagnostic Radiographic Procedure.” This code is used when no other specific procedure code exists. The existence of this CPT will support the initial marketing of the proposed solution. In Phase III, we will prepare an application for a new Category III CPT code and submit if for consideration by the American Medical Association (AMA) CPT Editorial Panel. Given the clinical need of the proposed solution, and recent approval of a CPT code for radiology artificial intelligence (code 0691T) for automated analysis of existing imaging studies for vertebral fracture and bone density assessment, our application for a new CPT code should not face any difficulties. In the longer term, the proposed imaging analysis technology can be used for automatic analysis of thousands of medical images that are taken every day in hospitals and clinics. This will enable detection of diseases and conditions at early stages (e.g., bone metastasis and different tumors), thus facilitating preventive measures and better care for those individuals at risk.

项目摘要 /摘要 大约有3个女性中有1名和50岁以上的5分之一的男性将遭受脆弱性骨折 剩余的一生。脆性髋部骨折是骨质疏松症的最严重和令人衰弱的结果之一 骨折后的第一年，死亡率为20–40％。已知髋部骨折的发生率会增加 男女的年龄呈指数级；随着全球预期寿命的提高， 预计将高于脆弱性骨折门槛的男人和女人的数量将几乎翻一番， 到2040年预测了3.19亿个病例。这也预计骨折的数量也将增加两倍。 在这个直接的II阶段SBIR中，生物密度与骨科，放射学，内分泌学和 哈佛医学院的生物力学专家，提议开发基于云的软件解决方案 使用普通X射线图像，称为XFX的自动机会筛选，以进行髋部骨折风险。 X射线研究是 世界各地无处不在，价格便宜，并提供了高分辨率研究，可洞悉 在低电离辐射剂量下的骨几何形状，微结构和密度。提出的软件解决方案将 包括1）用于上传X射线图像并显示和可视化XFX结果的桌面应用程序，以及2）A 安全的基于云的后端，用于接收上传的X射线图像并执行分析。软件 建筑将支持与医院云服务（例如PACS系统）的本地集成以启用 使用普通的X射线图像自动筛查髋部骨折风险。解决方案将在 医院或诊所的中央成像数据服务器，调查每个未投资的X射线图像，以及如果 公认包括近端股骨，自动执行基于AI/ML的分类方案以识别 骨质疏松症或髋部骨折的高风险。如果确定患者患有骨质疏松症或高 骨折的风险，该软件会标记患者。然后将提示为患者提供护理的临床护理 考虑订购脆弱性骨折风险的评估并收到完整的报告。 此过程可根据当前程序术语（CPT）代码76499报销 诊断放射学过程。“当不存在其他特定过程代码时，使用此代码。 该CPT的存在将支持拟议解决方案的初始营销。在第三阶段，我们将准备 申请新类别III CPT代码，并提交美国医学协会的考虑 （AMA）CPT社论面板。鉴于提议的解决方案的临床需求以及CPT代码的最新批准 用于放射学人工智能（代码0691T）用于椎骨现有成像研究的自动分析 骨折和骨密度评估，我们对新CPT代码的应用不应遇到任何困难。 从长远来看，提出的成像分析技术可用于自动分析数千个 每天在医院和诊所拍摄的医学图像。这将使疾病发现和 早期的疾病（例如骨转移和不同肿瘤），从而促进预防措施和 更好地照顾那些有危险的人。