Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Annalisa, Polidori"'
Autor:
Philip S. Salmon, Gregory S. Moody, YoshikiIshii, Keiron J. Pizzey, Annalisa Polidori, Mathieu Salanne, Anita Zeidler, Michela Buscemi, Henry E. Fischer, Craig L. Bull, Stefan Klotz, Richard Weber, Chris J. Benmore, Simon G. MacLeod
Publikováno v:
Journal of Non-Crystalline Solids: X, Vol 18, Iss , Pp 100154- (2023)
Externí odkaz:
https://doaj.org/article/59925799c5b14efea266d25fd602900a
Autor:
Isabella Castiglioni, Davide Ippolito, Matteo Interlenghi, Caterina Beatrice Monti, Christian Salvatore, Simone Schiaffino, Annalisa Polidori, Davide Gandola, Cristina Messa, Francesco Sardanelli
Publikováno v:
European Radiology Experimental, Vol 5, Iss 1, Pp 1-10 (2021)
Abstract Background We aimed to train and test a deep learning classifier to support the diagnosis of coronavirus disease 2019 (COVID-19) using chest x-ray (CXR) on a cohort of subjects from two hospitals in Lombardy, Italy. Methods We used for train
Externí odkaz:
https://doaj.org/article/4d8745fc0bc141d09463f1f7b3ad22b9
Autor:
Philip S. Salmon, Gregory S. Moody, Yoshiki Ishii, Keiron J. Pizzey, Annalisa Polidori, Mathieu Salanne, Anita Zeidler, Michela Buscemi, Henry E. Fischer, Craig L. Bull, Stefan Klotz, Richard Weber, Chris J. Benmore, Simon G. MacLeod
Publikováno v:
Journal of Non-Crystalline Solids: X, Vol 3, Iss , Pp - (2019)
The pressure-induced structural transformations in metasilicate MSiO3 glass (M = Mg or Ca) on cold-compression from ambient pressure to 17.5 GPa were investigated by neutron diffraction. The structure of the glass recovered to ambient conditions from
Externí odkaz:
https://doaj.org/article/928ce7d8a11b479eb4d41b304b56ec04
Autor:
Christian Salvatore, Matteo Interlenghi, Caterina B. Monti, Davide Ippolito, Davide Capra, Andrea Cozzi, Simone Schiaffino, Annalisa Polidori, Davide Gandola, Marco Alì, Isabella Castiglioni, Cristina Messa, Francesco Sardanelli
Publikováno v:
Diagnostics, Vol 11, Iss 3, p 530 (2021)
We assessed the role of artificial intelligence applied to chest X-rays (CXRs) in supporting the diagnosis of COVID-19. We trained and cross-validated a model with an ensemble of 10 convolutional neural networks with CXRs of 98 COVID-19 patients, 88
Externí odkaz:
https://doaj.org/article/94cb65d7a72742eeb33dada141570107
Autor:
Christian Salvatore, Mariagrazia D'Ippolito, Matteo Interlenghi, Antonio De Tanti, Silvia Premoselli, Anna Estraneo, Federico Scarponi, Orsola Masotta, Annamaria Romoli, Rita Formisano, Paolo Tonin, Bahia Hakiki, Diana Frattini, Chiara Bertolino, Annalisa Polidori, Mauro Zampolini, Lucia Francesca Lucca, Antonio Cerasa, Pamela Salucci, Francesca Cava
Publikováno v:
Journal of neurotrauma (Online) 38 (2020): 1988–1994. doi:10.1089/neu.2020.7302
info:cnr-pdr/source/autori:Lucca ML; De Tanti A; Cava F; Romoli A; Formisano R; Scarponi E; Estraneo A; Frattini D; Bertolino C; Salucci P; Di Tullio MG; D'Ippolito MG; Zampolini M; Masotta O; Premoselli S; Interlenghi M; Salvatore C; Polidori A; Tonin P; Cerasa A;/titolo:Predicting outcome of acquired brain injury by the evolution of Paroxysmal Sympathetic Hyperactivity signs/doi:10.1089%2Fneu.2020.7302/rivista:Journal of neurotrauma (Online)/anno:2020/pagina_da:1988/pagina_a:1994/intervallo_pagine:1988–1994/volume:38
info:cnr-pdr/source/autori:Lucca ML; De Tanti A; Cava F; Romoli A; Formisano R; Scarponi E; Estraneo A; Frattini D; Bertolino C; Salucci P; Di Tullio MG; D'Ippolito MG; Zampolini M; Masotta O; Premoselli S; Interlenghi M; Salvatore C; Polidori A; Tonin P; Cerasa A;/titolo:Predicting outcome of acquired brain injury by the evolution of Paroxysmal Sympathetic Hyperactivity signs/doi:10.1089%2Fneu.2020.7302/rivista:Journal of neurotrauma (Online)/anno:2020/pagina_da:1988/pagina_a:1994/intervallo_pagine:1988–1994/volume:38
In this multi-center study, we provide a systematic evaluation of the clinical variability associated with paroxysmal sympathetic hyperactivity (PSH) in patients with acquired brain injury (ABI) to determine how these signs can impact outcomes. A tot
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::24ce9dccb625708bfacdccbdfd54b71b
https://doi.org/10.1089/neu.2020.7302
https://doi.org/10.1089/neu.2020.7302
Autor:
Caterina Beatrice Monti, Simone Schiaffino, Francesco Sardanelli, Marco Alì, Davide Ippolito, Isabella Castiglioni, Cristina Messa, Davide Capra, Davide Gandola, Christian Salvatore, Matteo Interlenghi, Andrea Cozzi, Annalisa Polidori
Publikováno v:
Diagnostics
Diagnostics, Vol 11, Iss 530, p 530 (2021)
Diagnostics (Basel) 11 (2021): 530. doi:10.3390/diagnostics11030530
info:cnr-pdr/source/autori:Salvatore C.; Interlenghi M.; Monti C.B.; Ippolito D.; Capra D.; Cozzi A.; Schiaffino S.; Polidori A.; Gandola D.; Alì M.; Castiglioni I.; Messa C.; Sardanelli F./titolo:Artificial Intelligence Applied to Chest X-ray for Differential Diagnosis of COVID-19 Pneumonia/doi:10.3390%2Fdiagnostics11030530/rivista:Diagnostics (Basel)/anno:2021/pagina_da:530/pagina_a:/intervallo_pagine:530/volume:11
Volume 11
Issue 3
Diagnostics, Vol 11, Iss 530, p 530 (2021)
Diagnostics (Basel) 11 (2021): 530. doi:10.3390/diagnostics11030530
info:cnr-pdr/source/autori:Salvatore C.; Interlenghi M.; Monti C.B.; Ippolito D.; Capra D.; Cozzi A.; Schiaffino S.; Polidori A.; Gandola D.; Alì M.; Castiglioni I.; Messa C.; Sardanelli F./titolo:Artificial Intelligence Applied to Chest X-ray for Differential Diagnosis of COVID-19 Pneumonia/doi:10.3390%2Fdiagnostics11030530/rivista:Diagnostics (Basel)/anno:2021/pagina_da:530/pagina_a:/intervallo_pagine:530/volume:11
Volume 11
Issue 3
We assessed the role of artificial intelligence applied to chest X-rays (CXRs) in supporting the diagnosis of COVID-19. We trained and cross-validated a model with an ensemble of 10 convolutional neural networks with CXRs of 98 COVID-19 patients, 88
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2c2256948f89dc2ef4b5d4f86691d5e1
http://europepmc.org/articles/PMC8000736
http://europepmc.org/articles/PMC8000736
Autor:
Isabella Castiglioni 1, 2, Davide Ippolito 3, Matteo Interlenghi 2, Caterina Beatrice Monti 4, Christian Salvatore 5, 6, Simone Schiaffino 7, Annalisa Polidori 8, Davide Gandola 3, Cristina Messa 9, Francesco Sardanelli 4, 7
Publikováno v:
European radiology experimental Online 5 (2021): 7. doi:10.1186/s41747-020-00203-z.
info:cnr-pdr/source/autori:Isabella Castiglioni 1,2, Davide Ippolito 3, Matteo Interlenghi 2, Caterina Beatrice Monti 4, Christian Salvatore 5,6, Simone Schiaffino 7, Annalisa Polidori 8, Davide Gandola 3, Cristina Messa 9,10, Francesco Sardanelli 4,7/titolo:Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy/doi:10.1186%2Fs41747-020-00203-z./rivista:European radiology experimental Online/anno:2021/pagina_da:7/pagina_a:/intervallo_pagine:7/volume:5
info:cnr-pdr/source/autori:Isabella Castiglioni 1,2, Davide Ippolito 3, Matteo Interlenghi 2, Caterina Beatrice Monti 4, Christian Salvatore 5,6, Simone Schiaffino 7, Annalisa Polidori 8, Davide Gandola 3, Cristina Messa 9,10, Francesco Sardanelli 4,7/titolo:Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy/doi:10.1186%2Fs41747-020-00203-z./rivista:European radiology experimental Online/anno:2021/pagina_da:7/pagina_a:/intervallo_pagine:7/volume:5
Background: We aimed to train and test a deep learning classifier to support the diagnosis of coronavirus disease 2019 (COVID-19) using chest x-ray (CXR) on a cohort of subjects from two hospitals in Lombardy, Italy. Methods: We used for training and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=cnr_________::6f658c9ca80e2330e9ea894f2bba7137
https://publications.cnr.it/doc/444351
https://publications.cnr.it/doc/444351
Autor:
Matthew B. Stone, Yasuhiro Fujii, Ippei Obayashi, Akihiko Hirata, Yohei Onodera, Shinji Kohara, Alex C. Hannon, Philip S. Salmon, Alexander I. Kolesnikov, Seiji Kojima, Annalisa Polidori, Anita Zeidler, Koji Ohara, Hiroyuki Inoue, Jaakko Akola, Marshall T. McDonnell, Suguru Kitani, Osami Sakata, Atsunobu Masuno, Norimasa Nishiyama, Tatsuya Mori, Yasuaki Hiraoka, Henry E. Fischer, Shuta Tahara, Takenobu Nakamura, Matthew G. Tucker, Hitoshi Kawaji, Takashi Taniguchi, Motoki Shiga
Publikováno v:
NPG Asia Materials
'NPG Asia Materials ', vol: 12, pages: 85-1-85-16 (2020)
'NPG Asia Materials ', vol: 12, pages: 85-1-85-16 (2020)
The broken symmetry in the atomic-scale ordering of glassy versus crystalline solids leads to a daunting challenge to provide suitable metrics for describing the order within disorder, especially on length scales beyond the nearest neighbor that are
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8f729dfacad5efd1f3a0d96b4f6bb591
https://trepo.tuni.fi/handle/10024/128101
https://trepo.tuni.fi/handle/10024/128101
Publikováno v:
Polidori, A, Zeidler, A & Salmon, P 2020, ' Structure of As-Se glasses by neutron diffraction with isotope substitution ', Journal of Chemical Physics, vol. 153, no. 15, 154507 . https://doi.org/10.1063/5.0027171
The method of neutron diffraction with selenium isotope substitution is used to measure the structure of glassy As0.30Se0.70, As0.35Se0.65 and As0.40Se0.60. The method delivers three difference functions for each sample in which either the As-As, As-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f970a80885c8e32458915b6244da2619
https://pubmed.ncbi.nlm.nih.gov/33092375
https://pubmed.ncbi.nlm.nih.gov/33092375
Autor:
Christian Salvatore, Matteo Interlenghi, Davide Gandola, Francesco Sardanelli, Simone Schiaffino, Isabella Castiglioni, Annalisa Polidori, Cristina Messa, Caterina Beatrice Monti, Davide Ippolito
ObjectivesWe tested artificial intelligence (AI) to support the diagnosis of COVID-19 using chest X-ray (CXR). Diagnostic performance was computed for a system trained on CXRs of Italian subjects from two hospitals in Lombardy, Italy.MethodsWe used f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::24088bc4cc2a94f990a9ce507b222fda
https://doi.org/10.1101/2020.04.08.20040907
https://doi.org/10.1101/2020.04.08.20040907