18F‐fluorodeoxyglucose positron emission tomography (FDG‐PET) enables in‐vivo capture of the topographic metabolism patterns in the brain. These images have shown great promise in revealing the altered metabolism patterns in Alzheimer's disease (AD). The AD pathology is progressive, and leads to structural and functional alterations that lie on a continuum. There is a need to quantify the altered metabolism patterns that exist on a continuum into a simple measure. This work proposes a 3D convolutional neural network with residual connections that generates a probability score useful for interpreting the FDG‐PET images along the continuum of AD. This network is trained and tested on images of stable normal control and stable Dementia of the Alzheimer's type (sDAT) subjects, achieving an AUC of 0.976 via repeated fivefold cross‐validation. An independent test set consisting of images in between the two extreme ends of the DAT spectrum is used to further test the generalization performance of the network. Classification performance of 0.811 AUC is achieved in the task of predicting conversion of mild cognitive impairment to DAT for conversion time of 0–3 years. The saliency and class activation maps, which highlight the regions of the brain that are most important to the classification task, implicate many known regions affected by DAT including the posterior cingulate cortex, precuneus, and hippocampus.
Background: In recent years, many convolutional neural networks (CNN) have been proposed for the classification of Alzheimer’s disease. Due to memory constraints, many of the proposed CNNs work at a 2D slice-level or 3D patch-level. Objective: Here, we propose a subject-level 3D CNN that can extract the neurodegenerative patterns of the whole brain MRI and converted into a probabilistic Dementia score. Methods: We propose an efficient and lightweight subject-level 3D CNN featuring dilated convolutions. We trained our network on the ADNI data on stable Dementia of the Alzheimer’s type (sDAT) from stable normal controls (sNC). To comprehensively evaluate the generalizability of our proposed network, we performed four independent tests which includes testing on images from other ADNI individuals at various stages of the dementia, images acquired from other sites (AIBL), images acquired using different protocols (OASIS), and longitudinal images acquired over a short period of time (MIRIAD). Results: We achieved a 5-fold cross-validated balanced accuracy of 88%in differentiating sDAT from sNC, and an overall specificity of 79.5%and sensitivity 79.7%on the entire set of 7,902 independent test images. Conclusion: Independent testing is essential for estimating the generalization ability of the network to unseen data, but is often lacking in studies using CNN for DAT classification. This makes it difficult to compare the performances achieved using different architectures. Our comprehensive evaluation highlighting the competitive performance of our network and potential promise for generalization
Background: Advanced machine learning methods can aid in the identification of dementia risk using neuroimaging-derived features including FDG-PET. However, to enable the translation of these methods and test their usefulness in clinical practice, it is crucial to conduct independent validation on real clinical samples, which has yet to be properly delineated in the current literature. Objective: In this paper, we present our efforts to enable such clinical translational through the evaluation and comparison of two machine-learning methods for discrimination between dementia of Alzheimer’s type (DAT) and Non-DAT controls. Methods: FDG-PET-based dementia scores were generated on an independent clinical sample whose clinical diagnosis was blinded to the algorithm designers. A feature-engineered approach (multi-kernel probability classifier) and a non-feature-engineered approach (3D convolutional neural network) were analyzed. Both classifiers were pre-trained on cognitively normal subjects as well as subjects with DAT. These two methods provided a probabilistic dementia score for this previously unseen clinical data. Performance of the algorithms were compared against ground-truth dementia rating assessed by experienced nuclear physicians. Results: Blinded clinical evaluation on both classifiers showed good separation between the cognitively normal subjects and the patients diagnosed with DAT. The non-feature-engineered dementia score showed higher sensitivity among subjects whose diagnosis was in agreement between the machine-learning models, while the feature-engineered approach showed higher specificity in non-consensus cases. Conclusion: In this study, we demonstrated blinded evaluation using data from an independent clinical sample for assessing the performance in DAT classification models in a clinical setting. Our results showed good generalizability for two machine-learning approaches, marking an important step for the translation of pre-trained machine-learning models into clinical practice.
Background In recent years, many convolutional neural networks (CNN) have been proposed for the classification of Alzheimer's Disease (AD). Due to memory constraints, many of the proposed CNNs work at a 2D slice‐level or 3D patch‐level. Other subject‐level 3D CNNs which take a whole brain 3D MRI image as input require a long training time. Method Here, we propose a lightweight subject‐level 3D CNN featuring dilated convolutions which allow the receptive field to be increased efficiently through a small number of layers. To comprehensively evaluate the generalizability of our proposed network, we performed four independent tests which includes testing on images from other ADNI individuals at various stages of the dementia, images acquired from other databases/sites (AIBL), images acquired using different protocols (OASIS) and longitudinal images acquired over a short period of time (MIRIAD). Result We trained our network on the ADNI data, and we achieved a 5‐fold cross‐validated balanced accuracy of 88% in differentiating stable Dementia of the Alzheimer's type (sDAT) from stable normal controls (sNC). Our network showed 78.5% accuracy in classifying images of mild cognitive impairment (MCI) subjects acquired 2 years prior to conversion to DAT. We achieved an overall specificity of 79.5% and sensitivity 79.7% on the entire set of 7902 independent test images Conclusion In this study, we constructed a lightweight 3D CNN network that converts the subject‐level image into a single AD dementia score to represent the disease progression. For estimating the generalization ability of the network to unseen data, independent testing is essential but is often lacking in studies using CNN for DAT classification. This makes it difficult to compare the performances achieved using different architectures. The result of our study highlights the competitive performance of our network and potential promise for generalization.
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