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SCH: AI-Enhanced Multimodal Sensor-on-a-chip for Alzheimer's Disease Detection

SCH：用于阿尔茨海默病检测的人工智能增强型多模态芯片传感器

基本信息

批准号：
10685378
负责人：
Juejun Hu
金额：
$ 29.77万
依托单位：
PENNSYLVANIA STATE UNIVERSITY, THE
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-01 至 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10685378
关键词：
Academia Address Affect Algorithmic Software Alzheimer disease detection Alzheimer&apos s Disease Alzheimer&apos s disease diagnosis Alzheimer’s disease biomarker Amyloid Proteins Artificial Intelligence Binding Biological Markers Biosensing Techniques Biosensor Blood Body Fluids Cessation of life Collaborations Data Data Scientist Dementia Detection Devices Drug Industry Elderly Enzyme-Linked Immunosorbent Assay Feedback Fiber Optics General Hospitals Goals Health Human Immunohistochemistry Impaired cognition Interdisciplinary Study Knowledge Learning Machine Learning Magnetic Resonance Imaging Mass Spectrum Analysis Massachusetts Measures Mechanics Memory Methods Mining Mission Modality Modeling Nanotechnology Neurodegenerative Disorders Optics Output Patients Performance Personal Satisfaction Personality Positioning Attribute Raman Spectrum Analysis Research Research Personnel Saliva Scientist Sensitivity and Specificity Signal Transduction Software Tools Somatotype Source Code Statistical Data Interpretation System Techniques Training Weight Western Blotting Work analytical tool apolipoprotein E-4 artificial intelligence algorithm biomarker discovery biomarker identification cantilever cost data repository deep learning algorithm design design verification detection method detection platform effectiveness evaluation flexibility health care service organization heterogenous data improved innovation insight machine learning framework machine learning method medical schools minimally invasive multimodality nanosensors novel optical fiber photonics programs sensor specific biomarkers tau Proteins tomography two-dimensional waveguide

项目摘要

We propose a new research paradigm aimed at addressing scientific questions in both biosensing and machine learning for the early prediction of Alzheimer's disease (AD), and at solving a grand challenge in the identification of minimally-invasive AD biomarkers in tear, saliva, and blood. Our goal is to develop a novel and minimally-invasive system that integrates a multimodal biosensing platform and a machine learning framework, which synergistically work together to significantly enhance the detection accuracy. The program will pioneer a novel Multimodal Optical, Mechanical, Electrochemical Nano-sensor with Twodimensional material Amplification (MOMENTA) platform for sensitive and selective detection of AD biomarkers. The sensor outputs are used for training the new Hierarchical Multimodal Machine Learning (HMML) framework, which not only automatically integrates the heterogeneous data from different modalities but also ranks the importance of different biosensors and biomarkers for AD prediction. Moreover, the framework is able to identify potential new biomarkers based on a statistical analysis of the learned weights on the input signals and provide feedback information to further improve the MOMENTA platform design. This interdisciplinary research brings together materials scientists who create new twodimensional (2D) material platforms for sensor enhancement, nanotechnology and device experts who advance chip-scale sensor platforms, data scientists who analyze data with machine learning methods to target early prediction of AD, and AD experts who help to identify potentially new AD biomarkers. The machine-learning-enhanced multi-modal sensor system will not only offer major performance boost compared to state-of-the-art, but also yield critical insights on new biomarker discovery for AD diagnosis at an early stage.

我们提出了一个新的研究范式，旨在解决生物传感和机器学习用于阿尔茨海默病（AD）的早期预测，并在解决一个巨大的挑战，在泪液、唾液和血液中鉴定微创AD生物标志物。我们的目标是发展一个一种新型的微创系统，它集成了多模态生物传感平台和机器，学习框架，协同工作，显着提高检测精度。该计划将开创一种新颖的多模式光学，机械，电化学纳米传感器与二维材料扩增（MOMENTA）平台，用于AD的灵敏和选择性检测生物标志物。传感器输出用于训练新的分层多模态机器学习（HMML）框架，它不仅可以自动集成来自不同的异构数据，模式，而且还对不同生物传感器和生物标志物对于AD预测的重要性进行排名。此外，该框架能够基于对生物标志物的统计分析来识别潜在的新生物标志物。学习输入信号的权重，并提供反馈信息，以进一步提高动量平台设计这种跨学科的研究汇集了材料科学家谁创造新的二维 (2D)传感器增强材料平台，纳米技术和设备专家，先进的芯片级传感器平台，数据科学家用机器学习方法分析数据，针对AD的早期预测，以及帮助识别潜在的新AD生物标志物的AD专家。的机器学习增强的多模态传感器系统不仅将提供重大的性能提升，与最先进的技术相比，还对AD诊断的新生物标志物发现产生了重要的见解，早期阶段。