[HTML][HTML] Estimating explainable Alzheimer's disease likelihood map via clinically-guided prototype learning
Identifying Alzheimer's disease (AD) involves a deliberate diagnostic process owing to its
innate traits of irreversibility with subtle and gradual progression. These characteristics make …
innate traits of irreversibility with subtle and gradual progression. These characteristics make …
[HTML][HTML] Identifying underlying patterns in Alzheimer's disease trajectory: a deep learning approach and Mendelian randomization analysis
Background Alzheimer's disease (AD) is a heterogeneously progressive neurodegeneration
disorder with varied rates of deterioration, either between subjects or within different stages …
disorder with varied rates of deterioration, either between subjects or within different stages …
[HTML][HTML] Explainability of random survival forests in predicting conversion risk from mild cognitive impairment to Alzheimer's disease
Abstract Random Survival Forests (RSF) has recently showed better performance than
statistical survival methods as Cox proportional hazard (CPH) in predicting conversion risk …
statistical survival methods as Cox proportional hazard (CPH) in predicting conversion risk …
Conversion from mild cognitive impairment to Alzheimer's disease: a comparison of tree-based machine learning algorithms for survival analysis
Prediction of conversion from Mild Cognitive Impairment (MCI) to Alzheimer's disease (AD)
is usually performed with Machine Learning (ML) supervised approaches. However, typical …
is usually performed with Machine Learning (ML) supervised approaches. However, typical …
[HTML][HTML] Constructing personalized characterizations of structural brain aberrations in patients with dementia using explainable artificial intelligence
Deep learning approaches for clinical predictions based on magnetic resonance imaging
data have shown great promise as a translational technology for diagnosis and prognosis in …
data have shown great promise as a translational technology for diagnosis and prognosis in …
[HTML][HTML] Essential New Complex-Based Themes for Patient-Centered Diagnosis and Treatment of Dementia and Predementia in Older People: Multimorbidity and …
E Wertman - Journal of Clinical Medicine, 2024 - mdpi.com
Dementia is a highly prevalent condition with devastating clinical and socioeconomic
sequela. It is expected to triple in prevalence by 2050. No treatment is currently known to be …
sequela. It is expected to triple in prevalence by 2050. No treatment is currently known to be …
[HTML][HTML] Differential diagnosis of frontotemporal dementia subtypes with explainable deep learning on structural MRI
Background Frontotemporal dementia (FTD) represents a collection of neurobehavioral and
neurocognitive syndromes that are associated with a significant degree of clinical …
neurocognitive syndromes that are associated with a significant degree of clinical …
Predicting Alzheimer's Disease Diagnosis Risk Over Time with Survival Machine Learning on the ADNI Cohort
The rise of Alzheimer's Disease worldwide has prompted a search for efficient tools which
can be used to predict deterioration in cognitive decline leading to dementia. In this paper …
can be used to predict deterioration in cognitive decline leading to dementia. In this paper …
Predicting progression to clinical Alzheimer's disease dementia using the random survival forest
Predicting Progression to Clinical Alzheimer’s Disease Dementia Using the Random Survival
Forest - IOS Press You are viewing a javascript disabled version of the site. Please enable …
Forest - IOS Press You are viewing a javascript disabled version of the site. Please enable …
[HTML][HTML] Early detection of dementia with default-mode network effective connectivity
Altered functional connectivity precedes structural brain changes and symptoms in
dementia. Alzheimer's disease is the largest contributor to dementia at the population level …
dementia. Alzheimer's disease is the largest contributor to dementia at the population level …