Data-driven modelling of neurodegenerative disease progression: thinking outside the black box
Data-driven disease progression models are an emerging set of computational tools that
reconstruct disease timelines for long-term chronic diseases, providing unique insights into …
reconstruct disease timelines for long-term chronic diseases, providing unique insights into …
Revolution of Alzheimer precision neurology. Passageway of systems biology and neurophysiology
The Precision Neurology development process implements systems theory with system
biology and neurophysiology in a parallel, bidirectional research path: a combined …
biology and neurophysiology in a parallel, bidirectional research path: a combined …
Predicting the progression of mild cognitive impairment using machine learning: a systematic, quantitative and critical review
We performed a systematic review of studies focusing on the automatic prediction of the
progression of mild cognitive impairment to Alzheimer's disease (AD) dementia, and a …
progression of mild cognitive impairment to Alzheimer's disease (AD) dementia, and a …
A precision medicine initiative for Alzheimer's disease: the road ahead to biomarker-guided integrative disease modeling
After intense scientific exploration and more than a decade of failed trials, Alzheimer's
disease (AD) remains a fatal global epidemic. A traditional research and drug development …
disease (AD) remains a fatal global epidemic. A traditional research and drug development …
Generative adversarial registration for improved conditional deformable templates
Deformable templates are essential to large-scale medical image registration, segmentation,
and population analysis. Current conventional and deep network-based methods for …
and population analysis. Current conventional and deep network-based methods for …
Forecasting individual progression trajectories in Alzheimer's disease
The anticipation of progression of Alzheimer's disease (AD) is crucial for evaluations of
secondary prevention measures thought to modify the disease trajectory. However, it is …
secondary prevention measures thought to modify the disease trajectory. However, it is …
Image prediction of disease progression for osteoarthritis by style-based manifold extrapolation
Disease-modifying management aims to prevent deterioration and progression of the
disease, and not just to relieve symptoms. We present a solution for the management by a …
disease, and not just to relieve symptoms. We present a solution for the management by a …
A multivariate nonlinear mixed effects model for longitudinal image analysis: Application to amyloid imaging
It is important to characterize the temporal trajectories of disease-related biomarkers in order
to monitor progression and identify potential points of intervention. These are especially …
to monitor progression and identify potential points of intervention. These are especially …
A Bayesian mixed-effects model to learn trajectories of changes from repeated manifold-valued observations
We propose a generic Bayesian mixed-effects model to estimate the temporal progression of
a biological phenomenon from observations obtained at multiple time points for a group of …
a biological phenomenon from observations obtained at multiple time points for a group of …
Progression models for imaging data with longitudinal variational auto encoders
Disease progression models are crucial to understanding degenerative diseases. Mixed-
effects models have been consistently used to model clinical assessments or biomarkers …
effects models have been consistently used to model clinical assessments or biomarkers …