AIMC Topic: Alzheimer Disease
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XAI-Based Assessment of the AMURA Model for Detecting Amyloid-β and Tau Microstructural Signatures in Alzheimer's Disease.
IEEE journal of translational engineering in health and medicine
Jul 17, 2024
Brain microstructural changes already occur in the earliest phases of Alzheimer's disease (AD) as evidenced in diffusion magnetic resonance imaging (dMRI) literature. This study investigates the potential of the novel dMRI Apparent Measures Using Red...
Disentangling brain atrophy heterogeneity in Alzheimer's disease: A deep self-supervised approach with interpretable latent space.
NeuroImage
Jul 14, 2024
Alzheimer's disease (AD) is heterogeneous, but existing methods for capturing this heterogeneity through dimensionality reduction and unsupervised clustering have limitations when it comes to extracting intricate atrophy patterns. In this study, we p...
Improving brain atrophy quantification with deep learning from automated labels using tissue similarity priors.
Computers in biology and medicine
Jul 10, 2024
Brain atrophy measurements derived from magnetic resonance imaging (MRI) are a promising marker for the diagnosis and prognosis of neurodegenerative pathologies such as Alzheimer's disease or multiple sclerosis. However, its use in individualized ass...
Unbiased analysis of spatial learning strategies in a modified Barnes maze using convolutional neural networks.
Scientific reports
Jul 10, 2024
Assessment of spatial learning abilities is central to behavioral neuroscience and a useful tool for animal model validation and drug development. However, biases introduced by the apparatus, environment, or experimentalist represent a critical chall...
Self-Explainable Graph Neural Network for Alzheimer Disease and Related Dementias Risk Prediction: Algorithm Development and Validation Study.
JMIR aging
Jul 8, 2024
BACKGROUND: Alzheimer disease and related dementias (ADRD) rank as the sixth leading cause of death in the United States, underlining the importance of accurate ADRD risk prediction. While recent advancements in ADRD risk prediction have primarily re...
Predicting Alzheimer's disease from cognitive footprints in mid and late life: How much can register data and machine learning help?
International journal of medical informatics
Jul 3, 2024
BACKGROUND: Real-world data with decades-long medical records are increasingly available alongside the growing adoption of machine learning in healthcare research. We evaluated the performance of machine learning models in predicting the risk of Alzh...
Structure focused neurodegeneration convolutional neural network for modelling and classification of Alzheimer's disease.
Scientific reports
Jul 3, 2024
Alzheimer's disease (AD), the predominant form of dementia, is a growing global challenge, emphasizing the urgent need for accurate and early diagnosis. Current clinical diagnoses rely on radiologist expert interpretation, which is prone to human err...
A systematic literature review on the significance of deep learning and machine learning in predicting Alzheimer's disease.
Artificial intelligence in medicine
Jul 2, 2024
BACKGROUND: Alzheimer's disease (AD) is the most prevalent cause of dementia, characterized by a steady decline in mental, behavioral, and social abilities and impairs a person's capacity for independent functioning. It is a fatal neurodegenerative d...
Predicting Alzheimer's Disease Progression Using a Versatile Sequence-Length-Adaptive Encoder-Decoder LSTM Architecture.
IEEE journal of biomedical and health informatics
Jul 2, 2024
Detecting Alzheimer's disease (AD) accurately at an early stage is critical for planning and implementing disease-modifying treatments that can help prevent the progression to severe stages of the disease. In the existing literature, diagnostic test ...
Predicting changes in brain metabolism and progression from mild cognitive impairment to dementia using multitask Deep Learning models and explainable AI.
NeuroImage
Jun 26, 2024
BACKGROUND: The prediction of Alzheimer's disease (AD) progression from its early stages is a research priority. In this context, the use of Artificial Intelligence (AI) in AD has experienced a notable surge in recent years. However, existing investi...
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