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 …

The Precision Neurology development process implements systems theory with system
biology and neurophysiology in a parallel, bidirectional research path: a combined …

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 …

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 …

Deformable templates are essential to large-scale medical image registration, segmentation,
and population analysis. Current conventional and deep network-based methods for …

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 …

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 …

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 …

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 …

Disease progression models are crucial to understanding degenerative diseases. Mixed-
effects models have been consistently used to model clinical assessments or biomarkers …