Increasing Interoperability of Brain Morphometrics Using FHIR

使用 FHIR 提高大脑形态测量的互操作性

• 批准号: 10255591
• 负责人: Lee Sean Tirrell
• 金额: $ 14.95万
• 依托单位: CORTICOMETRICS, LLC
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2022-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10255591
• 关键词: Alzheimer' s Disease; Artificial Intelligence; Big Data; Brain; Caring; Case Study; Clinical; Common Data Element; Computer software; Data; Data Store; Decision Making; Digital Imaging and Communications in Medicine; Ecosystem; Educational workshop; Electronic Health Record; Epilepsy; Evaluation Reports; Evaluation Studies; Fast Healthcare Interoperability Resources; Goals; Health Information System; Health Status; Healthcare; Hospitals; Hydrocephalus; Image; Informatics; Information Systems; MRI Scans; Magnetic Resonance Imaging; Measurement; Medical; Medical Device; Medical Imaging; Medicine; Metadata; Multiple Sclerosis; Neurologic; Output; Parkinson Disease; Patient Care; Patients; Performance; Picture Archiving and Communication System; Public Health Informatics; Quality of Care; Radiology Specialty; Reporting; Rest; Software Tools; Specific qualifier value; Structure; System; Technology; Time; Traumatic Brain Injury; Update; Work; application programming interface; base; data access; data exchange; data quality; data sharing; design; experience; health care settings; health data; health information technology; imaging software; improved; intelligent algorithm; interoperability; nervous system disorder; neuroimaging; next generation; novel strategies; open source; open source tool; personalized medicine; programs; quantitative imaging; radiologist; research clinical testing; software development; symposium; tool

PROJECT SUMMARY With the rise of artificial intelligence (AI) algorithms in medicine, radiologists have new tools at their disposal to quantitatively assess imaging data. However, in order to unlock this potential, data needs to be shared easily and effectively between all parts of the health information technology (IT) system. The goal of this project is to reduce data access barriers by developing software to cleanly integrate medical imaging data stored in a radiology department’s picture archiving and communication systems (PACS) with the rest of patients’ electronic health record (EHR) using the Fast Healthcare Interoperability Resources (FHIR®) standard. CorticoMetrics will use our THINQ™ software as a medical device (SaMD) product to provide brain morphometrics derived from MR imaging data, and extend its functionality to output results in both Digital Imaging and Communications in Medicine structured reporting (DICOM-SR) and Health Level 7 Fast Healthcare Interoperability Resources (HL7 FHIR) compliant formats. Based off of the scientifically validated FreeSurfer suite of automated neuroimaging analysis software, THINQ provides measurements of brain structures that can aid in the care of neurological conditions such as Alzheimer's disease and dementia, traumatic brain injury, epilepsy, hydrocephalus, Parkinson's disease and multiple sclerosis. Output in FHIR and DICOM-SR formats will be validated and included in CorticoMetrics’ next FDA 510(k) submission of THINQ. Incorporating this information with the rest of the rest of a patient’s EHR will enable a seamless workflow for clinicians to make decisions more efficiently and accurately while also improving the performance of those with less experience. This project will develop and disseminate an open source software tool to interconvert neuroimaging data between formats used in academic settings (such as FreeSurfer’s MGH or Neuroimaging Informatics Technology Initiative (NIfTI)) with the standard formats used in health care settings (DICOM and FHIR). Common Data Elements (CDE) will be used to facilitate data sharing across studies where appropriate. The product will lead to an increase in interoperability of brain morphometrics, giving medical professionals access to key data directly in the EHR. While THINQ will serve as an initial use case of this technology, the conversion tool will be easily extensible to other use cases, and freely available to developers of the next generation of quantitative imaging software.

项目概要 随着人工智能 (AI) 算法在医学领域的兴起，放射科医生可以使用新工具来 然而，为了释放这种潜力，需要轻松共享数据。 该项目的目标是： 通过软件开发减少数据访问障碍，以干净地集成存储在 放射科的图片存档和通信系统 (PACS) 与其他患者的 使用快速医疗互操作性资源 (FHIR®) 标准的电子健康记录 (EHR)。 CorticoMetrics 将使用我们的 THINQ™ 软件作为医疗设备 (SaMD) 产品，为大脑提供 从 MR 成像数据导出形态测量，并将其功能扩展为以数字和数字形式输出结果 医学成像和通信结构化报告 (DICOM-SR) 和健康 7 级快速 医疗保健互操作性资源 (HL7 FHIR) 兼容格式基于经过科学验证的格式。 FreeSurfer 自动化神经影像分析软件套件 THINQ 提供大脑测量 有助于治疗阿尔茨海默病和痴呆等神经系统疾病的结构， FHIR 和多发性硬化症的创伤性脑损伤、癫痫、脑积水、帕金森病和多发性硬化症。 DICOM-SR 格式将经过验证并包含在 CorticoMetrics 下一次向 FDA 510(k) 提交的 THINQ 中。 将此信息与患者 EHR 的其余部分结合起来，将为患者提供无缝的工作流程 防御者能够更有效、更准确地做出决策，同时也提高防御者的表现 经验较少。 该项目将开发和传播一种开源软件工具来相互转换神经影像数据 学术环境中使用的格式之间（例如 FreeSurfer 的 MGH 或 Neuroimaging Informatics 技术倡议 (NIfTI)) 以及医疗保健环境中使用的标准格式（DICOM 和 FHIR）。 通用数据元素（CDE）将用于在适当的情况下促进跨研究的数据共享。 产品将提高大脑形态测量的互操作性，使医疗专业人员能够访问 虽然 THINQ 将作为该技术的初始用例，但转换将直接转化为 EHR 中的关键数据。 工具将很容易扩展到其他用例，并免费提供给下一代的开发人员 定量成像软件。