Adapt innovative deep learning methods from breast cancer to Alzheimers disease

采用从乳腺癌到阿尔茨海默病的创新深度学习方法

基本信息

批准号：
10713637
负责人：
Shandong Wu
金额：
$ 28.38万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-06-13 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10713637
关键词：
Affect Algorithms Alzheimer disease detection Alzheimer&apos s Disease Alzheimer&apos s disease patient Alzheimer’s disease biomarker American Amyloid beta-42 Area Artificial Intelligence Atrophic Autopsy Award Biopsy Brain Brain Injuries Characteristics Classification Clinical Cognitive Computing Methodologies Data Data Set Dementia Development Diagnosis Disease Early Diagnosis Early identification Early treatment Educational Curriculum Future Grant Image Impaired cognition Intelligence Knowledge Learning MRI Scans Machine Learning Magnetic Resonance Imaging Mammographic screening Measurement Medical Modality Modeling Nerve Degeneration Outcome Parents Positron-Emission Tomography Process Research Research Methodology Research Personnel Resources Sampling Scanning Spinal Puncture Spinal Tap Techniques Time Training Translating Triage United States National Institutes of Health Update Work biomarker development brain magnetic resonance imaging breast imaging cancer imaging cognitive function deep learning deep learning model design early detection biomarkers empowerment experience imaging biomarker imaging modality improved innovation learning strategy malignant breast neoplasm mild cognitive impairment multidisciplinary neuroimaging neuroimaging marker neuron loss non-invasive imaging outcome prediction predictive modeling prevent programs public health relevance radiomics research study risk prediction screening targeted treatment tau Proteins

项目摘要

Adapt innovative deep learning methods from breast cancer to Alzheimer’s disease Abstract Alzheimer’s disease (AD) is the most common form of dementia, with the number of affected Americans expected to reach 13.4 million by the year 2050. Early detection and treatment of AD is critical to prevent non- reversible and fatal brain damage. Thus, development of non-invasive markers from neuroimaging modalities (e.g., brain MRI) is of great significance for screening and early detection of AD. In the PI’s active R01 award (1R01EB032896-01), the research focuses on developing a new line of research strategy and technical innovation to analyze breast cancer images for diagnosis, risk prediction, and triage. The core strategy is to incorporate medical/clinical intelligence into data-driven deep learning modeling. This technical innovation is however not limited to breast cancer, but can be adapted to other diseases, such as AD, as well. Thus, in this Supplement proposal, we propose to develop an AD focus of our active R01 by adapting the new technical innovation in breast cancer into cognitive outcome prediction for discovering early and no-invasive imaging biomarkers for AD. The main task of this Supplement study is to build deep learning models using brain MRIs as input for cognitive outcome prediction, which is formulated as a typical classification problem among three cognitive classes: Normal Control vs. Mild Cognitive Impairment vs. AD. We proposed two specific aims: 1) Deep curriculum learning informed by samples’ characteristic knowledge for cognitive outcome prediction and 2) Learning knowledge from longitudinal brain MRIs to improve prediction of AD. We will mainly use the publicly available Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. We have assembled a multi- disciplinary team with complementary expertise. The proposed study will provide an avenue to translate some of the innovative techniques developed in other domains to advance non-invasive imaging biomarker development for AD. This project will also provide an opportunity for the PI’s team to get involved and contribute to AD-related new research.

采用从乳腺癌到阿尔茨海默病的创新深度学习方法抽象的阿尔茨海默病 (AD) 是最常见的痴呆症，受影响的美国人数量预计到 2050 年，这一数字将达到 1340 万。早期发现和治疗 AD 对于预防非 AD 至关重要因此，通过神经影像学方法开发非侵入性标记物。（如脑部MRI）对于AD的筛查和早期发现具有重要意义。在PI的主动R01奖中。（1R01EB032896-01），该研究的重点是开发新的研究策略和技术路线分析乳腺癌图像以进行诊断、风险预测和分诊的创新核心策略是。将医学/临床智能融入数据驱动的深度学习建模中。然而，不限于乳腺癌，还可以适应其他疾病，例如AD。补充提案，我们建议通过采用新技术来开发我们主动R01的AD焦点乳腺癌认知结果预测的创新，以发现早期和无创成像该补充研究的主要任务是使用大脑 MRI 建立深度学习模型。作为认知结果预测的输入，它被表述为三个之间的典型分类问题认知类别：正常控制、轻度认知障碍、AD 我们提出了两个具体目标：1) 深度学习课程根据样本的特征知识进行认知结果预测和 2）从纵向脑部 MRI 中学习知识来改进 AD 的预测。我们已经收集了公开的阿尔茨海默病神经影像倡议 (ADNI) 数据集。拟议的研究将提供一个途径来翻译一些专业知识。在其他领域开发的创新技术，以推进非侵入性成像生物标志物该项目还将为 PI 团队提供参与和开发的机会。为 AD 相关的新研究做出贡献。

项目成果

期刊论文数量（2）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Interpretable machine learning model for imaging-based outcome prediction after cardiac arrest.

用于心脏骤停后基于成像的结果预测的可解释机器学习模型。

DOI：
10.1016/j.resuscitation.2023.109894
发表时间：
2023-07-01
期刊：
Resuscitation
影响因子：
6.5
作者：
Chang Liu;J. Elmer;Dooman Arefan;M. Pease;Sh;ong Wu;ong
通讯作者：
ong

A self-training teacher-student model with an automatic label grader for abdominal skeletal muscle segmentation.

带有自动标签分级器的自训练师生模型，用于腹部骨骼肌分割。

DOI：
发表时间：
2022-10
期刊：
影响因子：
0
作者：
Hao, Degan;Ahsan, Maaz;Salim, Tariq;Duarte;Esmaeel, Dadashzadeh;Zhang, Yudong;Arefan, Dooman;Wu, Shandong
通讯作者：
Wu, Shandong