Výsledky vyhledávání - "Elsa D Angelini"

Akademický článek

Changes in neonatal admissions, care processes and outcomes in England and Wales during the COVID-19 pandemic: a whole population cohort study

Autor: Nicholas Longford, Sabita Uthaya, Cheryl Battersby, Sam F Greenbury, Kayleigh Ougham, Elsa D Angelini

Publikováno v: BMJ Open, Vol 11, Iss 10 (2021)

Objectives The COVID-19 pandemic instigated multiple societal and healthcare interventions with potential to affect perinatal practice. We evaluated population-level changes in preterm and full-term admissions to neonatal units, care processes and ou

Externí odkaz: https://doaj.org/article/8db4b73612f549af8574f80d7dfeb682

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Akademický článek

Fibrosis severity scoring on Sirius red histology with multiple-instance deep learning

Autor: Sneha N. Naik, Roberta Forlano, Pinelopi Manousou, Robert Goldin, Elsa D. Angelini

Publikováno v: Biological Imaging, Vol 3 (2023)

Non-alcoholic fatty liver disease (NAFLD) is now the leading cause of chronic liver disease, affecting approximately 30% of people worldwide. Histopathology reading of fibrosis patterns is crucial to diagnosing NAFLD. In particular, separating mild f

Externí odkaz: https://doaj.org/article/81c943f736f04a5cb4661b69716f413e

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Quantification of lung ventilation defects on hyperpolarized MRI: The Multi-Ethnic Study of Atherosclerosis (MESA) COPD study

Autor: Xuzhe Zhang, Elsa D. Angelini, Fateme S. Haghpanah, Andrew F. Laine, Yanping Sun, Grant T. Hiura, Stephen M. Dashnaw, Martin R. Prince, Eric A. Hoffman, Bharath Ambale-Venkatesh, Joao A. Lima, Jim M. Wild, Emlyn W. Hughes, R. Graham Barr, Wei Shen

Publikováno v: Magnetic Resonance Imaging. 92:140-149

To develop an end-to-end deep learning (DL) framework to segment ventilation defects on pulmonary hyperpolarized MRI.The Multi-Ethnic Study of Atherosclerosis Chronic Obstructive Pulmonary Disease (COPD) study is a nested longitudinal case-control st

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2d5734eed0201f9543edca91fb24dbfe
https://doi.org/10.1016/j.mri.2022.06.016

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Smart Learning of Click and Refine for Nuclei Segmentation on Histology Images

Autor: Antoine Habis, Vannary Meas-Yedid, Daniel Felipe Gonzalez Obando, Jean-Christophe Olivo-Marin, Elsa D. Angelini

Publikováno v: 2022 IEEE International Conference on Image Processing (ICIP)
2022 IEEE International Conference on Image Processing (ICIP), Oct 2022, Bordeaux, France. pp.2281-2285, ⟨10.1109/ICIP46576.2022.9897496⟩

International audience; Deep learning has proven to be a very efficient tool to help pathologists analyze Whole Slide Images (WSI) toward automated classification or segmentation of detailed structures such as nuclei, glands or glomeruli. These objec

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9c7688c1cb5c8b716fcef25ce3336615
https://hal-pasteur.archives-ouvertes.fr/pasteur-03927669

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Identification of variation in nutritional practice in neonatal units in England and association with clinical outcomes using agnostic machine learning

Autor: Chris Gale, Sam F. Greenbury, Cheryl Battersby, Kayleigh Ougham, Elsa D. Angelini, Neena Modi, Jinyi Wu

Publikováno v: Scientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
Scientific Reports

We used agnostic, unsupervised machine learning to cluster a large clinical database of information on infants admitted to neonatal units in England. Our aim was to obtain insights into nutritional practice, an area of central importance in newborn c

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::18c22669237d994bda43379324e1888d
https://doaj.org/article/57c4b1daaf4645168fb16db8a186d777

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Changes in neonatal admissions, care processes and outcomes in England and Wales during the COVID-19 pandemic: a whole population cohort study

Autor: Cheryl Battersby, Sabita Uthaya, Sam F. Greenbury, Nicholas T. Longford, Elsa D. Angelini, Kayleigh Ougham, Neena Modi

Publikováno v: BMJ Open
BMJ Open, Vol 11, Iss 10 (2021)

ObjectivesThe COVID-19 pandemic instigated multiple societal and healthcare interventions with potential to affect perinatal practice. We evaluated population-level changes in preterm and full-term admissions to neonatal units, care processes and out

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::31dbeb9fbd4f107d671de39240c7c5e8
https://pubmed.ncbi.nlm.nih.gov/34598993

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Birthweight and patterns of postnatal weight gain in very and extremely preterm babies in England and Wales, 2008-19: a cohort study

Autor: Elsa D. Angelini, Sabita Uthaya, Sam F Greenbury, Chris Gale, Kayleigh Ougham, Neena Modi, Cheryl Battersby

Summary Background Intrauterine and postnatal weight are widely regarded as biomarkers of fetal and neonatal wellbeing, but optimal weight gain following preterm birth is unknown. We aimed to describe changes over time in birthweight and postnatal we

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b3832a7d322fc2c9707000d0713d8995
http://hdl.handle.net/10044/1/91418

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Novel Subtypes of Pulmonary Emphysema Based on Spatially-Informed Lung Texture Learning: The Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study

Autor: Benjamin M. Smith, R. Graham Barr, Elsa D. Angelini, Andrew F. Laine, Jie Yang, Pallavi Balte, John H.M. Austin, Eric A. Hoffman

Publikováno v: IEEE transactions on medical imaging

Pulmonary emphysema overlaps considerably with chronic obstructive pulmonary disease (COPD), and is traditionally subcategorized into three subtypes previously identified on autopsy. Unsupervised learning of emphysema subtypes on computed tomography

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b0774a7ce31065475066109e4519b087
https://pubmed.ncbi.nlm.nih.gov/34224349

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Unsupervised Clustering Of Airway Tree Structures On High-Resolution CT: The Mesa Lung Study

Autor: Eric A. Hoffman, Artur Wysoczanski, R. Graham Barr, Grant Hiura, Yifei Sun, Andrew F. Laine, Elsa D. Angelini, Benjamin M. Smith

Publikováno v: ISBI

The morphology of the proximal human airway tree is highly variable in the general population, and known variants in airway branching patterns are associated with increased risk of COPD and with polymorphisms in growth factors involved in pulmonary d

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2b385fc05a228ead0097e42f8cf0f416
https://doi.org/10.1109/isbi48211.2021.9434172

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