RNN-Based Alzheimer's Disease Prediction from Prodromal Stage using Diffusion Tensor Imaging

Velazquez, Matthew; Anantharaman, R; Velazquez, Salma; Lee, Yugyung

doi:10.1109/bibm47256.2019.8983391

Cited by 13 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the future, we would like to combine this clinical features dependent model with our prior diffusion tensor imaging model [15] in order to create an ensemble predictor that can handle a large variety of available patient information. This would allow for greater flexibility for patient input data while maintaining high accuracy in the prediction.…”

Section: Discussionmentioning

confidence: 99%

Random forest model for feature-based Alzheimer’s disease conversion prediction from early mild cognitive impairment subjects

Velazquez

Lee

2020

Preprint

Self Cite

View full text Add to dashboard Cite

Alzheimer’s Disease (AD) conversion prediction from the mild cognitive impairment (MCI) stage has been a difficult challenge. This study focuses on providing an individualized MCI to AD conversion prediction using a balanced random forest model that leverages clinical data. In order to do this, 383 Early Mild Cognitive Impairment (EMCI) patients were gathered from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Of these patients, 49 would eventually convert to AD (EMCI_C), whereas the remaining 335 did not convert (EMCI_NC). All of these patients were split into training and testing data sets with 95 patients reserved for testing. Nine clinical features were selected, comprised of a mix of demographic, brain volume, and cognitive testing variables. Oversampling was then performed in order to balance the initially imbalanced classes. Our results showed that a random forest model was effective (93.6% accuracy) at predicting the conversion of EMCI patients to AD based on these clinical features. Additionally, we assessed the importance of each clinical feature at both the individual and model level for interpretation of the prediction itself. Our model could impact the clinical environment as a tool to predict the conversion to AD from a prodromal stage or to identify ideal candidates for clinical trials.

show abstract

Section: Discussionmentioning

confidence: 99%

Random forest model for feature-based Alzheimer’s disease conversion prediction from early mild cognitive impairment subjects

Velazquez

Lee

2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In some studies, part of the CNN architecture was inspired by Hosseini-Asl et al ( 2018 ), they provide a pre-trained 3DCNN network that learns to capture generic features of AD biomarkers and adapts to datasets from different domains. There are also studies using RNN to train an AD classifier (Velazquez et al, 2019 ). Cheng and Liu ( 2017 ) uses extracted inter-slice features to perform the final classification.…”

Section: Related Workmentioning

confidence: 99%

Combined Multi-Atlas and Multi-Layer Perception for Alzheimer's Disease Classification

Hong

Huang²,

Lin³

et al. 2022

Front. Aging Neurosci.

View full text Add to dashboard Cite

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. To distinguish the stage of the disease, AD classification technology challenge has been proposed in Pattern Recognition and Computer Vision 2021 (PRCV 2021) which provides the gray volume and average cortical thickness data extracted in multiple atlases from magnetic resonance imaging (MRI). Traditional methods either train with convolutional neural network (CNN) by MRI data to adapt the spatial features of images or train with recurrent neural network (RNN) by temporal features to predict the next stage. However, the morphological features from the challenge have been extracted into discrete values. We present a multi-atlases multi-layer perceptron (MAMLP) approach to deal with the relationship between morphological features and the stage of the disease. The model consists of multiple multi-layer perceptron (MLP) modules, and morphological features extracted from different atlases will be classified by different MLP modules. The final vote of all classification results obtains the predicted disease stage. Firstly, to preserve the diversity of brain features, the most representative atlases are chosen from groups of similar atlases, and one atlas is selected in each group. Secondly, each atlas is fed into one MLP to fetch the score of the classification. Thirdly, to obtain more stable results, scores from different atlases are combined to vote the result of the classification. Based on this approach, we rank 10th among 373 teams in the challenge. The results of the experiment indicate as follows: (1) Group selection of atlas reduces the number of features required without reducing the accuracy of the model; (2) The MLP architecture achieves better performance than CNN and RNN networks in morphological features; and (3) Compared with other networks, the combination of multiple MLP networks has faster convergence of about 40% and makes the classification more stable.

show abstract

“…In the future, we would like to combine this clinical features dependent model with our prior diffusion tensor imaging model [21] in order to create an ensemble predictor that can handle a large variety of available patient information. This would allow for greater flexibility for patient input data while maintaining high accuracy in the prediction.…”

Section: Plos Onementioning

confidence: 99%

Random forest model for feature-based Alzheimer’s disease conversion prediction from early mild cognitive impairment subjects

Velazquez

Lee

Initiative³

2021

PLoS ONE

Self Cite

View full text Add to dashboard Cite

Alzheimer’s Disease (AD) conversion prediction from the mild cognitive impairment (MCI) stage has been a difficult challenge. This study focuses on providing an individualized MCI to AD conversion prediction using a balanced random forest model that leverages clinical data. In order to do this, 383 Early Mild Cognitive Impairment (EMCI) patients were gathered from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Of these patients, 49 would eventually convert to AD (EMCI_C), whereas the remaining 334 did not convert (EMCI_NC). All of these patients were split randomly into training and testing data sets with 95 patients reserved for testing. Nine clinical features were selected, comprised of a mix of demographic, brain volume, and cognitive testing variables. Oversampling was then performed in order to balance the initially imbalanced classes prior to training the model with 1000 estimators. Our results showed that a random forest model was effective (93.6% accuracy) at predicting the conversion of EMCI patients to AD based on these clinical features. Additionally, we focus on explainability by assessing the importance of each clinical feature. Our model could impact the clinical environment as a tool to predict the conversion to AD from a prodromal stage or to identify ideal candidates for clinical trials.

show abstract

RNN-Based Alzheimer's Disease Prediction from Prodromal Stage using Diffusion Tensor Imaging

Cited by 13 publications

References 21 publications

Random forest model for feature-based Alzheimer’s disease conversion prediction from early mild cognitive impairment subjects

Random forest model for feature-based Alzheimer’s disease conversion prediction from early mild cognitive impairment subjects

Combined Multi-Atlas and Multi-Layer Perception for Alzheimer's Disease Classification

Random forest model for feature-based Alzheimer’s disease conversion prediction from early mild cognitive impairment subjects

Contact Info

Product

Resources

About