AUTOMATED DEIDENTIFICATION OF PATHOLOGY AND RADIOLOGY DATA

病理学和放射学数据的自动去识别

• 批准号: 10904320
• 负责人: LAWRENCE O'SULLIVAN
• 金额: $ 199.84万
• 依托单位: IMPACT BUSINESS INFORMATION SOLUTIONS INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2025-08-14
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10904320
• 关键词: Algorithms; Burn injury; Businesses; Contracts; Data; Data Set; Detection; Digital Imaging and Communications in Medicine; Event; Excision; Generations; Goals; Image; Letters; Manuals; Measurement; Metadata; Notification; Pathology; Performance; Phase; Process; Published Comment; Radiology Specialty; Statistical Data Interpretation; System; Testing; Text; Uncertainty; Validation; data de-identification; deep learning model; human-in-the-loop; improved; interest; programs; success; trigger point; usability; whole slide imaging

The overall project objective is to optimize and automate the detection and redaction of PHI in radiology data (DICOM) and pathology data (Whole Slide Imaging - WSI) – both header metadata and “burned in” pixel data – thereby minimizing the need for human-in-the-loop review/manual redaction and maximizing deidentified data throughput. Impact Business Information Solutions, Inc (IBIS) has established four major goals for Phase II of this project. One, to enhance the deep learning models begun in Phase I, executing the algorithms against real-world data (as opposed to synthetic data, in Phase I), and incrementally improving their success rates. IBIS will also enhance the NLP capability for PHI detection in text with a more sophisticated algorithm. Two, to put in place a comprehensive human-in-the-loop workflow. This will require the addition of several new modules to EICON DEID including User Management, Access Control, and a Messaging subsystem for event-driven notifications. Three, to develop a more accurate automated measurement of de-identification confidence using an uncertainty quantification algorithm, which will be used to determine with informed precision the trigger point for initiation of the human-in-the-loop process. And four, to productize and validate the EICON DEID solution with a strong focus on system performance, scalability, usability and regulatory compliance.

总体项目目标是优化和自动化放射学数据（DICOM）和病理学数据（全幻灯片成像-WSI）中PHI的检测和修复 - 标头元数据和“燃烧”像素数据 - 从而最大程度地减少了对人的手动审查/手动修复/最大程度地限制确认数据的数据。 Impact Business Information Solutions，Inc（IBIS）为该项目的第二阶段建立了四个主要目标。第一，为了增强深度学习模型，从第一阶段开始，执行针对现实世界数据的算法（与合成数据相反），并逐步提高了其成功率。 IBI还将通过更复杂的算法增强文本中PHI检测的NLP能力。二，建立一个全面的 人类在循环工作流程。这将需要向EICON DEID增加几个新模块，包括用户管理，访问控制和事件驱动的通知的消息传递子系统。第三，使用不确定性量化算法来开发更准确的自动测量去识别置信度，该算法将以知情的精度来确定启动人类在循环过程的触发点。和四个，用A生产和验证EICON DEID溶液 强烈关注系统性能，可伸缩性，可用性和法规合规性。