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  • Amelia’s Publications

Amelia Cannon

Master Thesis Student

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Amelia Cannon, 21, holds a Bachelor’s degree in Human Medicine from the ETH Zurich and is currently on a gap year before continuing her medical education at the University of Zurich.

Her academic journey has sparked a strong interest in translational research in neurological diseases, with a focus on imaging techniques and the intersection of medicine and computational research.

Amelia has worked on a variety of systematic revies, encompassing diverse topics such as motoneuron disease animal models and multiple sclerosis (MS) misdiagnosis. During a research exchange with a collaborator at the National Institutes of Health (NIH) in the USA (Prof. Daniel S. Reich), Amelia delved into advanced neuroimaging techniques in MS. Concretely, she is assessing the clinical relevance of enlarged perivascular spaces (EPVS) as a potential imaging biomarker in multiple sclerosis, their temporal evolution and assessing the sensitivity of higher field strength MRI to detect them. Additionally, she is also contributing to a project aimed at automatically extracting key information from abstracts in the field of animal research for neurological diseases.

Outside of her professional pursuits, Amelia enjoys immersing herself in new experiences, from moving abroad for internships to solo travels across Southeast Asia, finding joy in broadening her horizons. Amelia has joined the STRIDE-lab as research assistant in September 2022.

Education

MA Medicine | University of Zurich
BSc Human Medicine | ETH Zurich


Amelia’s Publications

Auto-STEED: A data mining tool for automated extraction of experimental parameters and risk of bias items from in vivo publications

Auto-STEED: A data mining tool for automated extraction of experimental parameters and risk of bias items from in vivo publications
Zürrer WE, Cannon AE, Ewing E, Brüschweiler D, Bugajska J, Hild BF, Rosso M, Reich DS, Ineichen BV
biorxiv (2023)

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Neuroimaging findings in preclinical amyotrophic lateral sclerosis models-How well do they mimic the clinical phenotype? A systematic review.

Neuroimaging findings in preclinical amyotrophic lateral sclerosis models-How well do they mimic the clinical phenotype? A systematic review.
Cannon AE, Zürrer WE, Zejlon C, Kulcsar Z, Lewandowski S, Piehl F, Granberg T, Ineichen BV*
Frontiers in Veterinary Sciences, 10:1135282. Epub 20230502. 2023. (2023)

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The Brainbox - a tool to facilitate correlation of brain magnetic resonance imaging features to histopathology.

The Brainbox - a tool to facilitate correlation of brain magnetic resonance imaging features to histopathology.
Faigle W, Piccirelli M, Hortobágyi T, Frontzek K, Cannon AE, Zürrer WE, Granberg T, Kulcsar Z, Ludersdorfer T, Frauenknecht KBM, Reimann R, Ineichen BV
Brain Communication. 5(6): fcad307. 2023. (2023)

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NeuroTrialNER: An Annotated Corpus for Neurological Diseases and Therapies in Clinical Trial Registries

NeuroTrialNER: An Annotated Corpus for Neurological Diseases and Therapies in Clinical Trial Registries
Doneva SE, Ellendorff TR, Sick B, Goldman JP, Cannon AE, Schneider G, Ineichen BV
EMNLP (2024) (2024)

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Misdiagnosis and underdiagnosis of multiple sclerosis: A systematic review and meta-analysis.

Misdiagnosis and underdiagnosis of multiple sclerosis: A systematic review and meta-analysis.
Zürrer WE, Cannon AE, Ilchenko D, Gaitán MI, Granberg T, Piehl F, Solomon AJ, Ineichen BV
Mult Scler 30(11-12): 1409-1422 (2024)

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STEED: A data mining tool for automated extraction of experimental parameters and risk of bias items from in vivo publications.

STEED: A data mining tool for automated extraction of experimental parameters and risk of bias items from in vivo publications.
Zurrer WE, Cannon AE, Ewing E, Brüschweiler D, Bugajska J, Hild BF, Rosso M, Reich DS, Ineichen BV
PLoS One 19(11): e0311358 (2024)

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