AIMC Topic: Disease Progression

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Deep recurrent model for individualized prediction of Alzheimer's disease progression.

NeuroImage
Alzheimer's disease (AD) is known as one of the major causes of dementia and is characterized by slow progression over several years, with no treatments or available medicines. In this regard, there have been efforts to identify the risk of developin...

Hybrid dilation and attention residual U-Net for medical image segmentation.

Computers in biology and medicine
Medical image segmentation is a typical task in medical image processing and critical foundation in medical image analysis. U-Net is well-liked in medical image segmentation, but it doesn't fully explore useful features of the channel and capitalize ...

Predicting eyes at risk for rapid glaucoma progression based on an initial visual field test using machine learning.

PloS one
OBJECTIVE: To assess whether machine learning algorithms (MLA) can predict eyes that will undergo rapid glaucoma progression based on an initial visual field (VF) test.

Deep learning approach to predict pain progression in knee osteoarthritis.

Skeletal radiology
OBJECTIVE: To develop and evaluate deep learning (DL) risk assessment models for predicting pain progression in subjects with or at risk of knee osteoarthritis (OA).

Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data.

Nature communications
Multiple sclerosis (MS) can be divided into four phenotypes based on clinical evolution. The pathophysiological boundaries of these phenotypes are unclear, limiting treatment stratification. Machine learning can identify groups with similar features ...

Early Coaching to Increase Water Intake in CKD.

Annals of nutrition & metabolism
INTRODUCTION: In observational studies, increased water intake improves kidney function but not in adults with CKD stage 3 and more. CKD WIT trial has shown a nonsignificant gradual decline in kidney function after 1 year of coaching to increase wate...

Predicting Parkinson's disease trajectory using clinical and neuroimaging baseline measures.

Parkinsonism & related disorders
INTRODUCTION: Predictive biomarkers of Parkinson's Disease progression are needed to expedite neuroprotective treatment development and facilitate prognoses for patients. This work uses measures derived from resting-state functional magnetic resonanc...

Investigating the relationship between the SNCA gene and cognitive abilities in idiopathic Parkinson's disease using machine learning.

Scientific reports
Cognitive impairments are prevalent in Parkinson's disease (PD), but the underlying mechanisms of their development are unknown. In this study, we aimed to predict global cognition (GC) in PD with machine learning (ML) using structural neuroimaging, ...