Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer’s Disease using structural MR and FDG-PET images

By Donghuan Lu, Karteek Popuri, Gavin Weiguang Ding, Rakesh Balachandar, Mirza Faisal Beg. 2017

348 citations recorded by the source corpus.

Alzheimer’s Disease (AD) is a progressive neurodegenerative disease where biomarkers for disease based on pathophysiology may be able to provide objective measures for disease diagnosis and staging. Neuroimaging scans acquired from MRI and metabolism images obtained by FDG-PET provide in-vivo measurements of structure and function (glucose metabolism) in a living brain. It is hypothesized that combining multiple different image modalities providing complementary information could help improve early diagnosis of AD. In this paper, we propose a novel deep-learning-based framework to discriminate individuals with AD utilizing a multimodal and multiscale deep neural network. Our method delivers 82.4% accuracy in identifying the individuals with mild cognitive impairment (MCI) who will convert to AD at 3 years prior to conversion (86.4% combined accuracy for conversion within 1–3 years), a 94.23% sensitivity in classifying individuals with clinical diagnosis of probable AD, and a 86.3% specificity in classifying non-demented controls improving upon results in published literature.

Subfields: Neurology, Psychiatry and Mental health, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Neurology

1. Arvid Lundervold
2. Dan Boneh
3. Florian Tramèr

In Psychiatry and Mental health

1. Arvid Lundervold

In Radiology, Nuclear Medicine and Imaging

1. Di Guo
2. Arvid Lundervold
3. Simon Denman
4. Erica Schmidt
5. Clinton Fookes
6. Xiaobo Qu
7. Maithra Raghu
8. Florian Tramèr

Citations in the corpus

• An overview of deep learning in medical imaging focusing on MRI (2018, methodology): “[276] Multimodal and multiscale deep neural networks for the early diagnosis of AD using structural MR and”
• A Survey of Deep Learning for Scientific Discovery (2020, background): “…learn useful hidden representations of the image data in general, so after training with self-supervision, this neural network can then be successfully and efficiently finetuned on a downstream task. simultaneously trained on data from different modalities, such as images and text [139, 238, 102].”
• Deep Learning for Medical Anomaly Detection – A Survey (2020, background): “[82] Fusion of MRI and FDG-PET Detection of Alzheimer’s Disease Alzheimer’s Disease Neuroimaging Initiative (ADNI) database [61] 82.”
• DEMNET: A Deep Learning Model for Early Diagnosis of Alzheimer Diseases and Dementia From MR Images (2021, positive): “Lu et al. [12] proposed a novel multimodal deep neural network with a multistage technique to identify people with dementia.”
• Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer’s Disease Prediction From Mild Cognitive Impairment (2018, methodology): “Although an accuracy of 82.51% was also obtained with PET data (Lu et al., 2018b), PET scanning usually suffers from contrast agents and more expensive cost than the routine MRI.”