TOPIC 427 - De-Identification Software Tools and Pipelines for Cancer Imaging Research

主题 427 - 用于癌症成像研究的去识别化软件工具和管道

• 批准号: 10612710
• 负责人: LAWRENCE O'SULLIVAN
• 金额: $ 4.36万
• 依托单位: IMPACT BUSINESS INFORMATION SOLUTIONS INC.
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-16 至 2022-09-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10612710
• 关键词: Address; Algorithms; Clinical; Complex; Computer software; Data; Data Element; Development; Digital Imaging and Communications in Medicine; Head; Health; Image; Location; Manufacturer Name; Medical Imaging; Phase; Process; Reporting; Research; Security; Software Tools; System; Testing; Validation; base; cancer imaging; cloud based; data de-identification; deep learning

This proposal has three main objectives. Firstly, to identify actual and potential PHI/PII locations in DICOM and WSI data - both tags in the header data and coordinates in the image data, including manufacturer specific data - and to deliver a detailed, broad-based landscape analysis report. Secondly, to deliver a robust, validatable software solution that facilitates the development, management, chaining, and execution of medical image data de-identification algorithms. For Phase I, we will implement data de-identification pipelines using as a baseline the data elements identified in the landscape analysis report. Thirdly, to develop, on the same software platform, a set of deep learning-based algorithms to perform image data deidentification. We will facilitate the execution of these algorithms against data both local and remote, in order to obviate any security concerns regarding having to move data to the cloud for de-identification - i.e., we will "bring the algorithms to the data". Our cloud-based solution, EICON REACH (Remote Execution of Algorithms for Clinical Health), provides the underpinning for this proposal. We will enhance its capabilities to address directly the objectives listed. At the end of Phase I, we will provide these capabilities in a fully tested, fully documented, validated solution with full audit trail of user actions and data transformations. EICON REACH can also serve as the basis for a Phase II proposal wherein we would undertake a broader and deeper effort to address the more complex issues related to identification, review and redaction of PHI in the image data.

这项建议有三个主要目标。首先，识别DICOM和WSI数据中的实际和潜在的PHI/PII位置-标题数据中的标签和图像数据中的坐标，包括制造商特定的数据-并提供详细的、基础广泛的景观分析报告。其次，提供一个强大的、可验证的软件解决方案，促进医学图像数据去识别算法的开发、管理、链接和执行。在第一阶段，我们会以景观分析报告所确定的数据元素为基准，实施数据去识别化管道。第三，在同一软件平台上开发一套基于深度学习的算法来执行图像数据去识别。我们将促进这些算法对本地和远程数据的执行，以消除有关必须将数据移动到云进行去识别的任何安全问题-即，我们将“把算法带到数据中”。我们基于云的解决方案EICON REACH（远程执行临床健康算法）为这一提议提供了基础。我们将加强其直接实现所列目标的能力。在第一阶段结束时，我们将在经过充分测试、充分记录、验证的解决方案中提供这些功能，并对用户操作和数据转换进行完整的审计跟踪。EICON REACH也可以作为第二阶段提案的基础，在第二阶段，我们将进行更广泛和更深入的努力，以解决与图像数据中PHI的识别、审查和编辑相关的更复杂的问题。