TOPIC #411 - PHASE I SBIR CONTRACT - INTELLIGENT IMAGE ANONYMIZATION WITH XNAT

话题

• 批准号: 10274066
• 负责人: TIMOTHY OLSEN
• 金额: $ 39.97万
• 依托单位: RADIOLOGICS, INC.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-16 至 2021-06-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10274066
• 关键词: Clinical Research; Clinical Trials; Computer software; Contracts; Data; Database Management Systems; Databases; Detection; Healthcare; Image; Industry Collaboration; Intelligence; Natural Language Processing; Phase; Radiology Specialty; Research; Resources; Risk; Secure; Services; Small Business Innovation Research Grant; Vision; base; clinical imaging; deep learning; design; image archival system; imaging informatics; neural network; prototype; research and development; tool

This Fast Track SBIR aims to implement comprehensive image anonymization within an enterprise imaging informatics platform built on XNAT. Our vision is for this platform to provide large healthcare enterprises with tools to generate secure research databases at scale that mirror their clinical image archives. These databases would then provide local academic and industry collaborators with a rich resource for clinical research and development of AI-powered applications. Thus, our proposed anonymization services are designed to be scalable, risk-based, and verifiable. The platform's AI-powered image anonymization will include automated detection of PHI using a deep learning based natural language processing engine and automated detection of PHI in image content using a convolutational neural network. The anonymization services will be integrated into Radiologics enterprise and clinical trial XNAT products.

这种快速的SBIR旨在在XNAT建立的企业成像信息学平台中实现全面的图像匿名化。 我们的愿景是让这个平台为大型医疗保健企业提供工具，以大规模生成安全的研究数据库，以反映其临床图像档案。 然后，这些数据库将为当地的学术和行业合作者提供丰富的资源，用于AI驱动应用程序的临床研究和开发。因此，我们提出的匿名服务旨在扩展，基于风险和可验证。该平台的AI驱动图像匿名化将包括使用基于深度学习的自然语言处理引擎自动检测PHI，并使用备用神经网络对图像内容中的PHI自动检测。 匿名服务将集成到放射线企业和临床试验XNAT产品中。