Comparison of Anatomical and Diffusion MRI for detecting Parkinson's Disease using Deep Convolutional Neural Network.
Comparison of Anatomical and Diffusion MRI for detecting Parkinson's Disease using Deep Convolutional Neural Network.
复制标题
使用深度卷积神经网络检测帕金森病的解剖和扩散 MRI 的比较。
DOI：
10.1109/embc40787.2023.10340792
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Thompson,PaulM
中科院分区：
文献类型：
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作者：
Chattopadhyay,Tamoghna;Singh,Amit;Laltoo,Emily;Boyle,ChristinaP;Owens-Walton,Conor;Chen,Yao-Liang;Cook,Philip;McMillan,Corey;Tsai,Chih-Chien;Wang,J-J;Wu,Yih-Ru;vanderWerf,Ysbrand;Thompson,PaulM
帕金森病（PD）是一种进行性神经退行性疾病，影响着全世界超过1000万人。与其他与年龄相关的疾病（如阿尔茨海默病）相比，帕金森病的脑萎缩和微结构异常往往更为微妙，因此人们对机器学习方法在放射扫描中检测帕金森病的效果感兴趣。基于卷积神经网络（cnn）的深度学习模型可以从原始MRI扫描中自动提取诊断有用的特征，但大多数基于cnn的深度学习模型只在t1加权脑MRI上进行了测试。在这里，我们研究了弥散加权MRI （dMRI）的附加价值——MRI的一种变体，对微结构组织特性敏感——作为基于cnn的PD分类模型的附加输入。我们的评估使用了来自3个独立队列的数据——来自长庚大学、宾夕法尼亚大学和PPMI数据集。我们对cnn进行了不同组合的训练，以找到最好的预测模型。尽管需要在更多样化的数据上进行测试，但来自dMRI的深度学习模型显示出PD分类的前景。临床相关性：本研究支持使用弥散加权图像作为解剖学图像的替代方法，用于基于人工智能的帕金森病检测。
Parkinson’s disease (PD) is a progressive neurodegenerative disease that affects over 10 million people worldwide. Brain atrophy and microstructural abnormalities tend to be more subtle in PD than in other age-related conditions such as Alzheimer’s disease, so there is interest in how well machine learning methods can detect PD in radiological scans. Deep learning models based on convolutional neural networks (CNNs) can automatically distil diagnostically useful features from raw MRI scans, but most CNN-based deep learning models have only been tested on T1-weighted brain MRI. Here we examine the added value of diffusion-weighted MRI (dMRI) - a variant of MRI, sensitive to microstructural tissue properties - as an additional input in CNN-based models for PD classification. Our evaluations used data from 3 separate cohorts - from Chang Gung University, the University of Pennsylvania, and the PPMI dataset. We trained CNNs on various combinations of these cohorts to find the best predictive model. Although tests on more diverse data are warranted, deep-learned models from dMRI show promise for PD classification.Clinical Relevance— This study supports the use of diffusion-weighted images as an alternative to anatomical images for AI-based detection of Parkinson’s disease.