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Alzheimer's disease detection using deep learning on neuroimaging: a systematic review
Alzheimer's disease (AD) is a pressing global issue, demanding effective diagnostic
approaches. This systematic review surveys the recent literature (2018 onwards) to …
approaches. This systematic review surveys the recent literature (2018 onwards) to …
Deep Learning Techniques for Automated Dementia Diagnosis Using Neuroimaging Modalities: A Systematic Review (2012-2023)
Dementia is a condition that often comes with aging and affects how people think,
remember, and behave. Diagnosing dementia early is important because it can greatly …
remember, and behave. Diagnosing dementia early is important because it can greatly …
Identification of profiles associated with conversions between the Alzheimer's disease stages, using a machine learning approach
V Dauphinot, M Laurent, M Prodel, A Civet… - Alzheimer's Research & …, 2024 - Springer
Background The identification of factors involved in the conversion across the different
Alzheimer's disease (AD) stages is crucial to prevent or slow the disease progression. We …
Alzheimer's disease (AD) stages is crucial to prevent or slow the disease progression. We …
Machine learning models identify predictive features of patient mortality across dementia types
Background Dementia care is challenging due to the divergent trajectories in disease
progression and outcomes. Predictive models are needed to flag patients at risk of near-term …
progression and outcomes. Predictive models are needed to flag patients at risk of near-term …
Deep learning for risk-based stratification of cognitively impaired individuals
Quantifying the risk of progression to Alzheimer's disease (AD) could help identify persons
who could benefit from early interventions. We used data from the Alzheimer's Disease …
who could benefit from early interventions. We used data from the Alzheimer's Disease …
Survival models and longitudinal medical events for hospital readmission forecasting
Background The rate of 30-day all-cause hospital readmissions can affect the funding a
hospital receives. An accurate and reliable readmission prediction model could save money …
hospital receives. An accurate and reliable readmission prediction model could save money …
Novel plasma protein biomarkers: A time-dependent predictive model for Alzheimer's disease
T Zhuang, Y Yang, H Ren, H Zhang, C Gao… - Archives of Gerontology …, 2025 - Elsevier
Background The accurate prediction of Alzheimer's disease (AD) is crucial for the efficient
management of its progression. The objective of this research was to construct a new risk …
management of its progression. The objective of this research was to construct a new risk …
Individualized estimated years from onset of Alzheimer's disease–related decline for adults with Down syndrome
W Silverman, SJ Krinsky‐McHale… - Alzheimer's & …, 2023 - Wiley Online Library
Abstract Introduction Adults with Down syndrome (DS) are at increased risk for Alzheimer's
disease (AD) and vary in their age of transition from AD preclinical to prodromal or more …
disease (AD) and vary in their age of transition from AD preclinical to prodromal or more …
Machine learning approach predicts probability of time to stage-specific conversion of Alzheimer's disease
Background: The progression of Alzheimer's disease (AD) varies in different patients at
different stages, which makes predicting the time of disease conversions challenging …
different stages, which makes predicting the time of disease conversions challenging …
Updated Models of Alzheimer's Disease with Deep Neural Networks
T Sakharova, S Mao… - Journal of Alzheimer's …, 2024 - journals.sagepub.com
Background: In recent years, researchers have focused on develo** precise models for
the progression of Alzheimer's disease (AD) using deep neural networks. Forecasting the …
the progression of Alzheimer's disease (AD) using deep neural networks. Forecasting the …