238 citations · 245 across the 9 of their papers we have counts for
9 papers
End-to-end Cortical Surface Reconstruction from Clinical Magnetic Resonance Images
Jesper Duemose Nielsen, Karthik Gopinath, Andrew Hoopes +7
Surface-based cortical analysis is valuable for a variety of neuroimaging tasks, such as spatial normalization, parcellation, and gray matter (GM) thickness estimation. However, mo…
Evaluating GPT's Capability in Identifying Stages of Cognitive Impairment from Electronic Health Data
Yu Leng, Yingnan He, Colin Magdamo +7
Identifying cognitive impairment within electronic health records (EHRs) is crucial not only for timely diagnoses but also for facilitating research. Information about cognitive im…
Recon-all-clinical: Cortical surface reconstruction and analysis of heterogeneous clinical brain MRI
Karthik Gopinath, Douglas N. Greve, Colin Magdamo +4
Surface-based analysis of the cerebral cortex is ubiquitous in human neuroimaging with MRI. It is crucial for cortical registration, parcellation, and thickness estimation. Traditi…
Leveraging Pre-trained and Transformer-derived Embeddings from EHRs to Characterize Heterogeneity Across Alzheimer's Disease and Related Dementias
Matthew West, Colin Magdamo, Lily Cheng +2
Alzheimer's disease is a progressive, debilitating neurodegenerative disease that affects 50 million people globally. Despite this substantial health burden, available treatments f…
Cortical analysis of heterogeneous clinical brain MRI scans for large-scale neuroimaging studies
Karthik Gopinath, Douglas N. Greve, Sudeshna Das +3
Surface analysis of the cortex is ubiquitous in human neuroimaging with MRI, e.g., for cortical registration, parcellation, or thickness estimation. The convoluted cortical geometr…
Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets
Benjamin Billot, Colin Magdamo, You Cheng +3
Every year, millions of brain MRI scans are acquired in hospitals, which is a figure considerably larger than the size of any research dataset. Therefore, the ability to analyse su…
NeuraHealth: An Automated Screening Pipeline to Detect Undiagnosed Cognitive Impairment in Electronic Health Records with Deep Learning and Natural Language Processing
Tanish Tyagi, Colin G. Magdamo, Ayush Noori +22
Dementia related cognitive impairment (CI) is a neurodegenerative disorder, affecting over 55 million people worldwide and growing rapidly at the rate of one new case every 3 secon…
Using Deep Learning to Identify Patients with Cognitive Impairment in Electronic Health Records
Tanish Tyagi, Colin G. Magdamo, Ayush Noori +22
Dementia is a neurodegenerative disorder that causes cognitive decline and affects more than 50 million people worldwide. Dementia is under-diagnosed by healthcare professionals -…
Natural Language Processing to Detect Cognitive Concerns in Electronic Health Records Using Deep Learning
Zhuoqiao Hong, Colin G. Magdamo, Yi-han Sheu +16
Dementia is under-recognized in the community, under-diagnosed by healthcare professionals, and under-coded in claims data. Information on cognitive dysfunction, however, is often…