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.

项目摘要 随着医学中人工智能（AI）算法的兴起，放射科医生可以使用新工具 定量评估成像数据。但是，为了解锁这种潜力，需要轻松共享数据 并在健康信息技术（IT）系统的所有部分之间有效。这个项目的目标是 通过开发软件来清洁存储在A中的医学成像数据来减少数据访问障碍 放射科与其他患者的图片归档和通信系统（PAC） 使用快速医疗保健互操作性资源（FHIR®）标准的电子健康记录（EHR）。 皮质计量学将使用我们的ThinQ™软件作为医疗设备（SAMD）来提供大脑 从MR成像数据得出的形态计量学，并将其功能扩展到输出导致两个数字的输出 医学结构化报告（DICOM-SR）和健康水平7快速的成像和通信 医疗保健互操作性资源（HL7 FHIR）符合性格式。基于经过科学验证的 自动神经影像分析软件的FreeSurfer套件，ThinQ提供了大脑的测量 可以帮助护理神经系统疾病的结构，例如阿尔茨海默氏病和痴呆， 创伤性脑损伤，癫痫，脑积水，帕金森氏病和多发性硬化症。在FHIR和 DICOM-SR格式将经过验证，并包括在CortiSometrics的下一个FDA 510（k）提交ThinQ中。 将这些信息与患者的其他EHR的其余部分结合在一起，将为无缝工作流程 临床医生可以更有效，更准确地做出决定，同时也提高了 更少的经验。 该项目将开发并传播开源软件工具，以互换神经影像数据 在学术环境中使用的格式之间（例如FreeSurfer的MGH或神经影像学信息学 技术计划（NIFTI））具有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整个研究的数据共享。这 产品将导致大脑形态计量学的互操作性增加，使医疗专业人员访问 直接在EHR中进行关键数据。虽然ThinQ将作为该技术的初始用例，但转换 工具将很容易扩展到其他用例中，并免费提供下一代的开发人员 定量成像软件。