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: 10622545
• 负责人: Shigang Chen
• 金额: $ 29.21万
• 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/10622545
• 关键词: Artificial Intelligence; Biomedical Computing; Biomedical Research; Classification; Cloud Computing; Complex; Consumption; Data; Data Set; Diagnosis; Disease; Foundations; Genetic; Hospitals; Image; Joint Prosthesis; Laws; 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; Universities; Work; accurate diagnosis; artificial neural network; biomedical informatics; cloud based; cloud storage; cost; data privacy; data sharing; deep learning; deep neural network; digital technology; distributed data; encryption; experimental study; 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，以更准确的诊断和以较低的成本进行更好的治疗。同时，现代数字和移动技术使大规模收集患者的信息变得更加容易。尽管“大”医学数据提供了建立深入学习人工神经网络（ANN）模型的前所未有的机会，以推动诸如帕金森氏病（PD）等复杂疾病的研究，但它也给患者数据隐私带来了独特的挑战。培训和连续精炼ANN模型的任务是来自数以万计的患者的数据，每个患者都有许多属性和图像，这是计算密集型且耗时的。将这种计算及其数据外包给云是可行的解决方案。但是，在云中执行ANN学习操作的问题，而没有从分布式来源泄漏任何患者数据的风险，但迄今为止仍然开放。该应用程序旨在开发基于随机正交转换的新型数据掩盖技术，以实现AI计算外包和数据共享，以下两个具体目的：1）对HIPERGERGER云中的数据掩盖进行两项实验研究，以进行PD预测和帕克诺斯式诊断； 2）建立实验研究中使用的ANN模型的数据隐私，推理准确性和培训性能的理论基础。跨学科项目团队结合了数据隐私，生物医学信息，机器学习和云计算的专家，以开发数据外包和共享AI驱动的PD研究的技术。拟议的研究将消除限制医学数据可访问性的主要障碍，并阻碍基于云的深度学习人工神经元网络进行生物医学研究的操作。预计该结果将在PD研究中产生更大的影响，以推进具有数据隐私保护的基于云的医学研究的理论和实施。