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Your search keyword '"Favaro, Alberto"' showing total 4 results
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4 results on '"Favaro, Alberto"'

1. Using imaging to combat a pandemic: rationale for developing the UK National COVID-19 Chest Imaging Database.

Author

Jacob J, Alexander D, Baillie JK, Berka R, Bertolli O, Blackwood J, Buchan I, Bloomfield C, Cushnan D, Docherty A, Edey A, Favaro A, Gleeson F, Halling-Brown M, Hare S, Jefferson E, Johnstone A, Kirby M, McStay R, Nair A, Openshaw PJM, Parker G, Reilly G, Robinson G, Roditi G, Rodrigues JCL, Sebire N, Semple MG, Sudlow C, Woznitza N, and Joshi I

Subjects
COVID-19, Communicable Diseases, Emerging diagnostic imaging, Coronavirus Infections epidemiology, Databases, Factual statistics & numerical data, Diagnostic Imaging methods, Diagnostic Imaging statistics & numerical data, Female, Humans, Male, Pneumonia, Viral epidemiology, Program Development, Radiography, Thoracic methods, Radiography, Thoracic statistics & numerical data, Tomography, X-Ray Computed methods, United Kingdom, Artificial Intelligence statistics & numerical data, Communicable Diseases, Emerging epidemiology, Coronavirus Infections diagnostic imaging, Pandemics statistics & numerical data, Pneumonia, Viral diagnostic imaging, Tomography, X-Ray Computed statistics & numerical data
Abstract

Competing Interests: Conflict of interest: J. Jacob reports fees from Boehringer Ingelheim and Roche unrelated to the current submission and is supported by a Clinical Research Career Development Fellowship 209553/Z/17/Z from the Wellcome Trust, and by the NIHR Biomedical Research Centre at University College London. Conflict of interest: D. Alexander has nothing to disclose. Conflict of interest: J.K. Baillie reports grants from DHSC National Institute of Health Research UK, Medical Research Council UK, Wellcome Trust, Fiona Elizabeth Agnew Trust, Intensive Care Society and Chief Scientist Office, during the conduct of the study. Conflict of interest: R. Berka is an Associate at Faculty; Faculty has a paid partnership with NHSX to build its new AI lab to help drive digital transformation and the use of AI in the NHS. This contract has been extended for setting up the platform environment that manages the data storage for the National COVID-19 Chest Imaging Database (NCCID); the platform is being made available by Faculty at zero licence cost. In addition, Faculty is contracted to support NHS England and Improvement as well as NHSX with its data response strategy to COVID-19, which includes developing dashboards, models and simulations to provide information to central government decision-makers. Conflict of interest: O. Bertolli is an Associate at Faculty; Faculty has a paid partnership with NHSX to build its new AI lab to help drive digital transformation and the use of AI in the NHS. This contract has been extended for setting up the platform environment that manages the data storage for the National COVID-19 Chest Imaging Database (NCCID); the platform is being made available by Faculty at zero licence cost. In addition, Faculty is contracted to support NHS England and Improvement as well as NHSX with its data response strategy to COVID-19, which includes developing dashboards, models and simulations to provide information to central government decision-makers. Conflict of interest: J. Blackwood has nothing to disclose. Conflict of interest: I. Buchan reports personal fees from and acting as advisor for AstraZeneca, grants from NIHR, and is a former employee of Microsoft, outside the submitted work. Conflict of interest: C. Bloomfield reports that NCIMI is funded through support from the Industry Strategy Challenge Fund, by Innovate UK grant 104688. Conflict of interest: D. Cushnan has nothing to disclose. Conflict of interest: A. Docherty has nothing to disclose. Conflict of interest: A. Edey has nothing to disclose. Conflict of interest: A. Favaro is a Lead Data Scientist at Faculty; Faculty has a paid partnership with NHSX to build its new AI lab to help drive digital transformation and the use of AI in the NHS. This contract has been extended for setting up the platform environment that manages the data storage for the National COVID-19 Chest Imaging Database (NCCID); the platform is being made available by Faculty at zero licence cost. In addition, Faculty is contracted to support NHS England and Improvement as well as NHSX with its data response strategy to COVID-19, which includes developing dashboards, models and simulations to provide information to central government decision-makers. Conflict of interest: F. Gleeson has nothing to disclose. Conflict of interest: M. Halling-Brown is an advisor to Google Health on the MAMMOTH project, and has a Visiting Professorship with University of Surrey as part of a research collaboration with Transpara. Conflict of interest: S. Hare has nothing to disclose. Conflict of interest: E. Jefferson reports grants from Medical Research Council (MRC), Health Data Research (HDR) UK, National Institute of Health Research (NIHR), Chief Scientist Office (CSO), Engineering and Physical Sciences Research Council (EPSRC), Health Foundation, Data Lab, Scottish Government, NHS Fife Health Board and EU Horizon 2020, during the conduct of the study. Conflict of interest: A. Johnstone has nothing to disclose. Conflict of interest: M. Kirby is a Principal at Faculty; Faculty has a paid partnership with NHSX to build its new AI lab to help drive digital transformation and the use of AI in the NHS. This contract has been extended for setting up the platform environment that manages the data storage for the National COVID-19 Chest Imaging Database (NCCID); the platform is being made available by Faculty at zero licence cost. In addition, Faculty is contracted to support NHS England and Improvement as well as NHSX with its data response strategy to COVID-19, which includes developing dashboards, models and simulations to provide information to central government decision-makers. Conflict of interest: R. McStay has nothing to disclose. Conflict of interest: A. Nair reports salary reimbursement from Biomedical Research Centre UCL, and is medical advisor to Aidence BV, outside the submitted work. Conflict of interest: P.J.M. Openshaw reports personal fees for consultancy work with Janssen, J and J and Sanofi; grants from MRC, European Union, NIHR Biomedical Research Centre and Wellcome Trust, collaborative grants with GSK, personal fees from the European Respiratory Society and an NIHR Senior Investigator Award outside the submitted work; in addition, P.J.M. Openshaw was President of the British Society for Immunology; this was an unpaid appointment but travel and accommodation at some meetings was provided by the Society. Conflict of interest: G. Parker is director and shareholder of Bioxydyn Limited, outside the submitted work. Conflict of interest: G. Reilly has nothing to disclose. Conflict of interest: G. Robinson has nothing to disclose. Conflict of interest: G. Roditi has nothing to disclose. Conflict of interest: J.C.L. Rodrigues has nothing to disclose. Conflict of interest: N. Sebire has nothing to disclose. Conflict of interest: M.G. Semple reports grants from DHSC National Institute of Health Research UK, Medical Research Council UK, Health Protection Research Unit in Emerging and Zoonotic Infections, and University of Liverpool, during the conduct of the study; and is minority owner of Integrum Scientific LLC, Greensboro, NC, USA, outside the submitted work. Conflict of interest: C. Sudlow has nothing to disclose. Conflict of interest: N. Woznitza reports grants from Cancer Research UK and Roy Castle Lung Cancer Foundation, personal fees from InHealth, outside the submitted work. Conflict of interest: I. Joshi has nothing to disclose.

Published
2020
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2. EAACI guidelines on environmental science in allergic diseases and asthma – Leveraging artificial intelligence and machine learning to develop a causality model in exposomics.

Author

Shamji, Mohamed H., Ollert, Markus, Adcock, Ian M., Bennett, Oscar, Favaro, Alberto, Sarama, Roudin, Riggioni, Carmen, Annesi‐Maesano, Isabella, Custovic, Adnan, Fontanella, Sara, Traidl‐Hoffmann, Claudia, Nadeau, Kari, Cecchi, Lorenzo, Zemelka‐Wiacek, Magdalena, Akdis, Cezmi A., Jutel, Marek, and Agache, Ioana

Subjects
ARTIFICIAL intelligence, MACHINE learning, ALLERGIES, ENVIRONMENTAL sciences, ASTHMA, ALLERGIC conjunctivitis
Abstract

Allergic diseases and asthma are intrinsically linked to the environment we live in and to patterns of exposure. The integrated approach to understanding the effects of exposures on the immune system includes the ongoing collection of large‐scale and complex data. This requires sophisticated methods to take full advantage of what this data can offer. Here we discuss the progress and further promise of applying artificial intelligence and machine‐learning approaches to help unlock the power of complex environmental data sets toward providing causality models of exposure and intervention. We discuss a range of relevant machine‐learning paradigms and models including the way such models are trained and validated together with examples of machine learning applied to allergic disease in the context of specific environmental exposures as well as attempts to tie these environmental data streams to the full representative exposome. We also discuss the promise of artificial intelligence in personalized medicine and the methodological approaches to healthcare with the final AI to improve public health. [ABSTRACT FROM AUTHOR]

Published
2023
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3. Towards nationally curated data archives for clinical radiology image analysis at scale: Learnings from national data collection in response to a pandemic

Author

Cushnan, Dominic, Berka, Rosalind, Bertolli, Ottavia, Williams, Peter, Schofield, Daniel, Joshi, Indra, Favaro, Alberto, Halling-Brown, Mark, Imreh, Gergely, Jefferson, Emily, Sebire, Neil J., Reilly, Gerry, Rodrigues, Jonathan C.L., Robinson, Graham, Copley, Susan, Malik, Rizwan, Bloomfield, Claire, Gleeson, Fergus, Crotty, Moira, Denton, Erika, Dickson, Jeanette, Leeming, Gary, Hardwick, Hayley E., Baillie, Kenneth, Openshaw, Peter J.M., Semple, Malcolm G., Rubin, Caroline, Howlett, Andy, Rockall, Andrea G., Bhayat, Ayub, Fascia, Daniel, Sudlow, Cathie, Jacob, Joseph, Alexander, Daniel, Alexander, Melissa, Ashley, Nicholas, Aziz, Hena, Babar, Judith L., Barbar, Ramona Rita, Bates, Angela, Bennett, Oscar, Bettany, Alison, Bhatia, Bahadar, Bowen, Angela, Bown, Hannah, Brookes, Doreen, Buckle, Stephen, Burfield, Paul, Burford, Paul, Hurst, John, Imperial College Healthcare NHS Trust- BRC Funding, National Institute for Health Research, and UK Research and Innovation

Subjects
coronavirus SARS-CoV-2 disease, medicine, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Computer applications to medicine. Medical informatics, R858-859.7, Library science, Health Informatics, Imaging, Health data, Health Information Management, Pandemic, National data, Public, Environmental & Occupational Health, Science & Technology, Health Policy, NCCID Collaborative, respiratory, artificial intelligence, radiology, Computer Science Applications, Opinion Piece, Health Care Sciences & Services, machine learning, Geography, general, Collation, Scale (social sciences), Health Policy & Services, Life Sciences & Biomedicine, Medical Informatics
Abstract

The prevalence of the coronavirus SARS-CoV-2 disease has resulted in the unprecedented collection of health data to support research. Historically, coordinating the collation of such datasets on a national scale has been challenging to execute for several reasons, including issues with data privacy, the lack of data reporting standards, interoperable technologies, and distribution methods. The coronavirus SARS-CoV-2 disease pandemic has highlighted the importance of collaboration between government bodies, healthcare institutions, academic researchers and commercial companies in overcoming these issues during times of urgency. The National COVID-19 Chest Imaging Database, led by NHSX, British Society of Thoracic Imaging, Royal Surrey NHS Foundation Trust and Faculty, is an example of such a national initiative. Here, we summarise the experiences and challenges of setting up the National COVID-19 Chest Imaging Database, and the implications for future ambitions of national data curation in medical imaging to advance the safe adoption of artificial intelligence in healthcare.

Published
2021

4. Automated steerable path planning for Deep Brain Stimulation safeguarding fiber tracts and deep grey matter nuclei

Author

Alice Segato, Valentina Pieri, Alberto Favaro, Marco Riva, Andrea Falini, Elena De Momi, Antonella Castellano, Segato, Alice, Pieri, Valentina, Favaro, Alberto, Riva, Marco, Falini, Andrea, De Momi, Elena, and Castellano, Antonella

Subjects
deep brain stimulation, path planning, steerable electrode, tractography, advanced diffusion MRI, Deep brain stimulation, Computer science, lcsh:Mechanical engineering and machinery, medicine.medical_treatment, tractography, Context (language use), lcsh:QA75.5-76.95, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Neuroimaging, Artificial Intelligence, medicine, lcsh:TJ1-1570, Computer vision, Motion planning, steerable electrode, path planning, Original Research, Robotics and AI, advanced diffusion MRI, medicine.diagnostic_test, business.industry, Magnetic resonance imaging, Robotics, Computer Science Applications, deep brain stimulation, Trajectory, lcsh:Electronic computers. Computer science, Artificial intelligence, business, 030217 neurology & neurosurgery, Tractography
Abstract

Deep Brain Stimulation (DBS) is a neurosurgical procedure consisting in the stereotactic implantation of stimulation electrodes to specific brain targets, such as deep gray matter nuclei. Current solutions to place the electrodes rely on rectilinear stereotactic trajectories (RTs) manually defined by surgeons, based on pre-operative images. An automatic path planner that accurately targets subthalamic nuclei (STN) and safeguards critical surrounding structures is still lacking. Also, robotically-driven curvilinear trajectories (CTs) computed on the basis of state-of-the-art neuroimaging would decrease DBS invasiveness, circumventing patient-specific obstacles. This work presents a new algorithm able to estimate a pool of DBS curvilinear trajectories for reaching a given deep target in the brain, in the context of the EU's Horizon EDEN2020 project. The prospect of automatically computing trajectory plans relying on sophisticated newly engineered steerable devices represents a breakthrough in the field of microsurgical robotics. By tailoring the paths according to single-patient anatomical constraints, as defined by advanced preoperative neuroimaging including diffusion MR tractography, this planner ensures a higher level of safety than the standard rectilinear approach. Ten healthy controls underwent Magnetic Resonance Imaging (MRI) on 3T scanner, including 3DT1-weighted sequences, 3Dhigh-resolution time-of-flight MR angiography (TOF-MRA) and high angular resolution diffusion MR sequences. A probabilistic q-ball residual-bootstrap MR tractography algorithm was used to reconstruct motor fibers, while the other deep gray matter nuclei surrounding STN and vessels were segmented on T1 and TOF-MRA images, respectively. These structures were labeled as obstacles. The reliability of the automated planner was evaluated; CTs were compared to RTs in terms of efficacy and safety. Targeting the anterior STN, CTs performed significantly better in maximizing the minimal distance from critical structures, by finding a tuned balance between all obstacles. Moreover, CTs resulted superior in reaching the center of mass (COM) of STN, as well as in optimizing the entry angle in STN and in the skull surface.

Published
2019

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