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.

该提案有三个主要目标。首先，要在DICOM和WSI数据中确定实际和潜在的PHI/PII位置 - 标题数据中的标签和图像数据中的坐标（包括制造商的特定数据），并提供基于详细的，基于广泛的景观分析报告。其次，提供了一个可靠的，有效的软件解决方案，以促进医疗图像数据去识别算法的开发，管理，链接和执行。对于第一阶段，我们将使用基准来实施数据识别管道，以示景观分析报告中确定的数据元素。第三，要在同一软件平台上开发一系列基于深度学习的算法，以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至云以进行识别的任何安全问题 - 即，我们将“将算法带入数据”。我们基于云的解决方案Eicon Reach（远程执行临床健康算法）为该提案提供了基础。我们将增强其直接解决列出的目标的能力。在第一阶段结束时，我们将在经过全面测试的，经过全面记录的，经过验证的解决方案中提供这些功能，并完整审核用户操作和数据转换。 Eicon覆盖范围还可以作为II期建议的基础，在这些建议中，我们将采取更广泛，更深层次的努力来解决与图像数据中PHI的识别，审查和修订有关的更复杂的问题。