TY - GEN
T1 - Comparison of Anatomical and Diffusion MRI for detecting Parkinson's Disease using Deep Convolutional Neural Network
AU - Chattopadhyay, Tamoghna
AU - Singh, Amit
AU - Laltoo, Emily
AU - Boyle, Christina P.
AU - Owens-Walton, Conor
AU - Chen, Yao-Liang
AU - Cook, Philip
AU - McMillan, Corey
AU - Tsai, Chih-Chien
AU - Wang, J. J.
AU - Wu, Yih-Ru
AU - van der Werf, Ysbrand
AU - Thompson, Paul M.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85179645659&origin=inward
UR - https://www.ncbi.nlm.nih.gov/pubmed/38083460
U2 - 10.1109/EMBC40787.2023.10340792
DO - 10.1109/EMBC40787.2023.10340792
M3 - Conference contribution
C2 - 38083460
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
BT - 2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023
Y2 - 24 July 2023 through 27 July 2023
ER -