AIMC Topic: Disease Progression

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A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging data.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: It is challenging at baseline to predict when and which individuals who meet criteria for mild cognitive impairment (MCI) will ultimately progress to Alzheimer's disease (AD) dementia.

DRRNet: Dense Residual Refine Networks for Automatic Brain Tumor Segmentation.

Journal of medical systems
Glioma is one of the most common and aggressive brain tumors. Segmentation and subsequent quantitative analysis of brain tumor MRI are routine and crucial for treatment. Due to the time-consuming and tedious manual segmentation, automatic segmentatio...

A machine learning approach to knee osteoarthritis phenotyping: data from the FNIH Biomarkers Consortium.

Osteoarthritis and cartilage
OBJECTIVE: Knee osteoarthritis (KOA) is a heterogeneous condition representing a variety of potentially distinct phenotypes. The purpose of this study was to apply innovative machine learning approaches to KOA phenotyping in order to define progressi...

Machine learning approaches to studying the role of cognitive reserve in conversion from mild cognitive impairment to dementia.

International journal of geriatric psychiatry
OBJECTIVES: The overall aim of the present study was to explore the role of cognitive reserve (CR) in the conversion from mild cognitive impairment (MCI) to dementia. We used traditional and machine learning (ML) techniques to compare converter and n...

Comparison and development of machine learning tools in the prediction of chronic kidney disease progression.

Journal of translational medicine
BACKGROUND: Urinary protein quantification is critical for assessing the severity of chronic kidney disease (CKD). However, the current procedure for determining the severity of CKD is completed through evaluating 24-h urinary protein, which is incon...

Prostate cancer detection using residual networks.

International journal of computer assisted radiology and surgery
PURPOSE: To automatically identify regions where prostate cancer is suspected on multi-parametric magnetic resonance images (mp-MRI).