Automated PET tracer classification in multi-site, multi-cohort studies using deep learning
Deep learning classifies brain PET radiotracers directly from scans across sites and cohorts, improving metadata quality control.
robel-k.-gebre
Robel K. Gebre
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Regional diffusion imaging measures to disentangle SVD-related hypertensive arteriopathy versus cerebral amyloid angiopathy
A study of diffusion MRI signatures linked to hypertensive arteriopathy and cerebral amyloid angiopathy in cerebral small vessel disease.
sheelakumari-raghavan
Precise disease heterogeneity and progression quantification in MSA and Parkinson's disease using machine learning
We developed a machine learning framework to quantify disease heterogeneity and progression in multiple system atrophy and Parkinson's disease using structural and diffusion MRI.
robel-k.-gebre
Chapter 8 - Artificial intelligence: Relevance and applications in dementia
A book chapter reviewing AI applications in dementia research including imaging biomarkers, machine learning classification, and disease progression modeling.
sheelakumari-raghavan
Precise Estimation Of Heterogenous Global Tau Burden In The Brain Sheds Light On Cognitive Resilience Mechanisms
We applied the THETA score to investigate cognitive resilience mechanisms in individuals with heterogeneous global tau burden.
robel-k.-gebre
Harmonization Remains Unresolved: The Case of White Matter Hyperintensity Using ComBat And Deming Regression
We evaluated ComBat and Deming regression for harmonizing white matter hyperintensity measurements across scanners and demonstrated that harmonization challenges persist.
calvin-d.-reyes
Diffusion Imaging Measures to Disentangle SVD-related Hypertensive Arteriopathy versus Cerebral Amyloid Angiopathy
We used diffusion imaging measures to distinguish between hypertensive arteriopathy and cerebral amyloid angiopathy contributions to small vessel disease.
sheelakumari-raghavan
Developing scanner change invariant brain age models for aging and dementia studies
We developed brain age prediction models designed to be invariant to MRI scanner changes for use in aging and dementia studies.
siddhartha-satpathi
Automatic Classification of Head MR Series
We developed an automatic classification system for head MR series to streamline neuroimaging data management.
robert-i.-reid
Can integration of Alzheimer's plasma biomarkers with MRI, cardiovascular, genetics, and lifestyle measures improve cognition prediction?
MRI, plasma biomarkers, genetics, cardiovascular risk, and lifestyle measures were compared for predicting cognitive decline.
robel-k.-gebre
Advancing Tau PET Quantification in Alzheimer Disease with Machine Learning: Introducing THETA, a Novel Tau Summary Measure
THETA is a machine-learning tau PET measure that captures the spatial pattern of tau deposition across the brain in Alzheimer's disease.
robel-k.-gebre
Mapping neuroimaging using artificial intelligence to detect hypersomnia and its neurobiological correlates
We applied AI methods to neuroimaging data to detect hypersomnia and characterize its neurobiological correlates.
diego-carvalho
Machine Learning Models of Polygenic Risk for Enhanced Prediction of Alzheimer Disease Endophenotypes
We developed machine learning models integrating polygenic risk scores for enhanced prediction of Alzheimer's disease imaging and fluid biomarker endophenotypes.
nathaniel-b.-gunter
Validation Of the Tau Heterogeneity Evaluation in Alzheimer's Disease (THETA) Score Using Longitudinal and Histopathology Data
We validated the THETA score using longitudinal PET data and histopathology, demonstrating its association with disease progression and neuropathological staging.
robel-k.-gebre
Identifying transition points in AD biomarkers using machine learning: A supplement to the reliable worsening method
We developed a machine learning method to identify transition points in Alzheimer's disease biomarkers as a supplement to the reliable worsening method.
robel-k.-gebre
Chronic Medical Conditions and Dementia Risk: Brain Age Models for Quantifying Impact and Understanding Mechanisms
We used brain age models to quantify the impact of chronic medical conditions on dementia risk.
xi-yu
NODDI May Be More Sensitive to Neurodegenerative Changes Than Structural MRI
We compared NODDI diffusion imaging with structural MRI for sensitivity to neurodegenerative changes.
xi-yu
Genetic risk scores enhance the diagnostic value of plasma biomarkers of brain amyloidosis
We demonstrated that genetic risk scores enhance the diagnostic accuracy of plasma biomarkers for detecting brain amyloidosis.
vijay-k.-ramanan
A Novel Approach to Encode Two-Way Epistatic Interactions Between Single Nucleotide Polymorphisms
We developed a novel method to encode two-way epistatic interactions between single nucleotide polymorphisms for machine learning applications in genetics.
nathaniel-gunter
Cross-scanner harmonization methods for structural MRI may need further work: A comparison study
A systematic comparison questions the reliability of widely used cross-scanner harmonization methods for structural MRI.
robel-k.-gebre
Detecting hip osteoarthritis on clinical CT: A deep learning application based on 2-D summation images derived from CT
We developed the first deep learning application for detecting hip osteoarthritis from 2-D summation images derived from routine clinical CT scans.
robel-k.-gebre
Preparation of filter by alkali activation of blast furnace slag and its application for dye removal
We developed a filter using alkali-activated blast furnace slag for dye removal from wastewater.
mohammad-amzad-hossain-bhuyan
Discrimination of Low-Energy Acetabular Fractures from Controls Using Computed Tomography-Based Bone Characteristics
We identified CT-based bone structural characteristics that discriminate patients with low-energy acetabular fractures from matched controls.
robel-k.-gebre
Structural risk factors for low-energy acetabular fractures
We identified structural risk factors for low-energy acetabular fractures in the elderly using computed tomography.
robel-k.-gebre