AIMC Topic: Atrophy
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Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: A machine learning approach.
NeuroImage
Dec 10, 2019
Accurate early diagnosis of neurodegenerative diseases represents a growing challenge for current clinical practice. Promisingly, current tools can be complemented by computational decision-support methods to objectively analyze multidimensional meas...
Hippocampal segmentation for brains with extensive atrophy using three-dimensional convolutional neural networks.
Human brain mapping
Oct 14, 2019
Hippocampal volumetry is a critical biomarker of aging and dementia, and it is widely used as a predictor of cognitive performance; however, automated hippocampal segmentation methods are limited because the algorithms are (a) not publicly available,...
DeepHarmony: A deep learning approach to contrast harmonization across scanner changes.
Magnetic resonance imaging
Jul 10, 2019
Magnetic resonance imaging (MRI) is a flexible medical imaging modality that often lacks reproducibility between protocols and scanners. It has been shown that even when care is taken to standardize acquisitions, any changes in hardware, software, or...
Atrophy of cerebellar peduncles in essential tremor: a machine learning-based volumetric analysis.
European radiology
Jun 3, 2019
BACKGROUND: Subtle cerebellar signs are frequently observed in essential tremor (ET) and may be associated with cerebellar dysfunction. This study aims to evaluate the macrostructural integrity of the superior, middle, and inferior cerebellar peduncl...
AVRA: Automatic visual ratings of atrophy from MRI images using recurrent convolutional neural networks.
NeuroImage. Clinical
May 25, 2019
Quantifying the degree of atrophy is done clinically by neuroradiologists following established visual rating scales. For these assessments to be reliable the rater requires substantial training and experience, and even then the rating agreement betw...
Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer's Disease Diagnosis Using Structural MRI.
IEEE transactions on pattern analysis and machine intelligence
Dec 21, 2018
Structural magnetic resonance imaging (sMRI) has been widely used for computer-aided diagnosis of neurodegenerative disorders, e.g., Alzheimer's disease (AD), due to its sensitivity to morphological changes caused by brain atrophy. Recently, a few de...
Machine Learning-based Individual Assessment of Cortical Atrophy Pattern in Alzheimer's Disease Spectrum: Development of the Classifier and Longitudinal Evaluation.
Scientific reports
Mar 7, 2018
To develop a new method for measuring Alzheimer's disease (AD)-specific similarity of cortical atrophy patterns at the individual-level, we employed an individual-level machine learning algorithm. A total of 869 cognitively normal (CN) individuals an...
Decoding diagnosis and lifetime consumption in alcohol dependence from grey-matter pattern information.
Acta psychiatrica Scandinavica
Jan 26, 2018
OBJECTIVE: We investigated the potential of computer-based models to decode diagnosis and lifetime consumption in alcohol dependence (AD) from grey-matter pattern information. As machine-learning approaches to psychiatric neuroimaging have recently c...
Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance.
Clinical science (London, England : 1979)
Jun 28, 2017
In the brain, enlarged perivascular spaces (PVS) relate to cerebral small vessel disease (SVD), poor cognition, inflammation and hypertension. We propose a fully automatic scheme that uses a support vector machine (SVM) to classify the burden of PVS ...
Rey's Auditory Verbal Learning Test scores can be predicted from whole brain MRI in Alzheimer's disease.
NeuroImage. Clinical
Dec 18, 2016
Rey's Auditory Verbal Learning Test (RAVLT) is a powerful neuropsychological tool for testing episodic memory, which is widely used for the cognitive assessment in dementia and pre-dementia conditions. Several studies have shown that an impairment in...
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