Robel Gebre
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    • Precise disease heterogeneity and progression quantification in MSA and Parkinson's disease using machine learning
    • Chapter 8 - Artificial intelligence: Relevance and applications in dementia
    • Automatic Classification of Head MR Series
    • Developing scanner change invariant brain age models for aging and dementia studies
    • Diffusion Imaging Measures to Disentangle SVD-related Hypertensive Arteriopathy versus Cerebral Amyloid Angiopathy
    • Harmonization Remains Unresolved: The Case of White Matter Hyperintensity Using ComBat And Deming Regression
    • Precise Estimation Of Heterogenous Global Tau Burden In The Brain Sheds Light On Cognitive Resilience Mechanisms
    • Can integration of Alzheimer's plasma biomarkers with MRI, cardiovascular, genetics, and lifestyle measures improve cognition prediction?
    • Advancing Tau PET Quantification in Alzheimer Disease with Machine Learning: Introducing THETA, a Novel Tau Summary Measure
    • Mapping neuroimaging using artificial intelligence to detect hypersomnia and its neurobiological correlates
    • Machine Learning Models of Polygenic Risk for Enhanced Prediction of Alzheimer Disease Endophenotypes
    • Chronic Medical Conditions and Dementia Risk: Brain Age Models for Quantifying Impact and Understanding Mechanisms
    • Identifying transition points in AD biomarkers using machine learning: A supplement to the reliable worsening method
    • Validation Of the Tau Heterogeneity Evaluation in Alzheimer's Disease (THETA) Score Using Longitudinal and Histopathology Data
    • NODDI May Be More Sensitive to Neurodegenerative Changes Than Structural MRI
    • Non-imaging measures to predict variability in white matter changes relevant to VCID
    • Vascular risk, gait, behavioral, and plasma indicators of VCID
    • Genetic risk scores enhance the diagnostic value of plasma biomarkers of brain amyloidosis
    • A Novel Approach to Encode Two-Way Epistatic Interactions Between Single Nucleotide Polymorphisms
    • White Matter Degeneration Pathways Associated With Tau Deposition in Alzheimer Disease
    • Cross-scanner harmonization methods for structural MRI may need further work: A comparison study
    • Detecting hip osteoarthritis on clinical CT: A deep learning application based on 2-D summation images derived from CT
    • Preparation of filter by alkali activation of blast furnace slag and its application for dye removal
    • Discrimination of Low-Energy Acetabular Fractures from Controls Using Computed Tomography-Based Bone Characteristics
    • Structural risk factors for low-energy acetabular fractures
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Structural risk factors for low-energy acetabular fractures

Oct 1, 2019·
Robel K. Gebre
,
et al.
· 0 min read
DOI
Abstract
We identified structural risk factors for low-energy acetabular fractures in the elderly using computed tomography.
Type
Journal article
Publication
Bone
publications
Last updated on Apr 6, 2026
← Discrimination of Low-Energy Acetabular Fractures from Controls Using Computed Tomography-Based Bone Characteristics Jan 1, 2021

© 2026 Robel Gebre. This work is licensed under CC BY NC ND 4.0

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