SCH: Enabling Data Outsourcing and Sharing for AI-powered Parkinson's Research

SCH：为人工智能驱动的帕金森病研究提供数据外包和共享

• 批准号: 10480884
• 负责人: Shigang Chen
• 金额: $ 29.43万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-03 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10480884
• 关键词: Artificial Intelligence; Biomedical Computing; Biomedical Research; Classification; Cloud Computing; Complex; Consumption; Data; Data Set; Diagnosis; Disease; Foundations; Image; Institutes; Joint Prosthesis; Laws; Lead; Machine Learning; Masks; Mathematics; Medical; Methods; Modeling; Modernization; Neural Network Simulation; Non-linear Models; Outcome; Outsourcing; Parkinson Disease; Parkinsonian Disorders; Patient Data Privacy; Patients; Performance; Privacy; Public Health Informatics; Quality of life; Randomized; Regulation; Research; Risk; Series; Source; Technology; Theoretical Studies; Time; Training; University Hospitals; Work; accurate diagnosis; artificial neural network; base; biomedical informatics; cloud based; cloud storage; cost; data privacy; data sharing; deep learning; deep neural network; digital; distributed data; encryption; experimental study; imaging genetics; improved; individualized medicine; learning network; mobile computing; novel; operation; privacy preservation; privacy protection; theories

Artificial intelligence holds the promise of transforming data-driven biomedical research and computational health informatics for more accurate diagnosis and better treatment at lower cost. In the meantime, modern digital and mobile technologies make it much easier to collect information from patients in large scale. While “big” medical data offers unprecedented opportunities of building deep-learning artificial neural network (ANN) models to advance the research of complex diseases such as Parkinson’s disease (PD), it also presents unique challenges to patient data privacy. The task of training and continuously refining ANN models with data from tens of thousands of patients, each with numerous attributes and images, is computation-intensive and time-consuming. Outsourcing such computation and its data to the cloud is a viable solution. However, the problem of performing the ANN learning operations in the cloud, without the risk of leaking any patient data from their distributed sources, remains open to date. This application proposes to develop novel data masking technologies based on randomized orthogonal transformation to enable AI-computation outsourcing and data sharing, with the following two specific aims: 1) Perform two experimental studies of training ANN models with data masking in the HiperGator cloud for PD prediction and Parkinsonism diagnosis; 2) establish the theoretical foundation on data privacy, inference accuracy, and training performance of the ANN models used in the experimental studies. The interdisciplinary project team combines the expertise from data privacy, biomedical informatics, machine learning, and cloud computing to develop data outsourcing and sharing technologies for AI-powered PD research. The proposed research will remove a major roadblock that restricts medical data accessibility and hinders cloud-based operations of deep-learning artificial neural networks for biomedical research. The outcome is expected to have a broader impact beyond PD research in advancing the theory and implementation of cloud-based medical studies with data privacy protection.

人工智能有望改变数据驱动的生物医学研究和计算健康信息学，以更低的成本实现更准确的诊断和更好的治疗，同时，现代数字和移动技术使大规模收集患者信息变得更加容易。”大”医疗数据为构建深度学习人工神经网络（ANN）模型以推进帕金森病（PD）等复杂疾病的研究提供了前所未有的机会，同时也对患者数据隐私的训练和持续任务提出了独特的挑战。使用以下数据完善 ANN 模型数以万计的患者，每个患者都有大量的属性和图像，将此类计算及其数据外包到云中是一个可行的解决方案，但是，在云中执行 ANN 学习操作的问题。没有从其分布式来源泄露任何患者数据的风险，该应用程序建议开发基于随机正交变换的新型数据屏蔽技术，以实现人工智能计算外包和数据共享，其具体目标如下：1。 ) 进行两个实验研究在 HiperGator 云中使用数据屏蔽训练 ANN 模型，用于 PD 预测和帕金森病诊断；2）为实验研究中使用的 ANN 模型的数据隐私、推理准确性和训练性能奠定理论基础。拟议的研究将利用数据隐私、生物医学信息学、机器学习和云计算的专业知识，为人工智能驱动的腹膜透析研究开发数据外包和共享技术，从而消除限制医疗数据访问和基于云的操作的主要障碍。用于生物医学研究的深度学习人工神经网络，预计其成果将在推进基于云的医学研究的理论和实施以及数据隐私保护方面产生比PD研究更广泛的影响。