Consensus on innovations and future directions of community first responder schemes in United Kingdom: a national nominal group technique study.

Autor: Patel G; Community and Health Research Unit, School of Health and Care Sciences, University of Lincoln, Lincoln, UK., Botan V; Community and Health Research Unit, School of Health and Care Sciences, University of Lincoln, Lincoln, UK., Phung VH; Community and Health Research Unit, School of Health and Care Sciences, University of Lincoln, Lincoln, UK., Trueman I; Community and Health Research Unit, School of Health and Care Sciences, University of Lincoln, Lincoln, UK., Hosseini MP; Community and Health Research Unit, School of Health and Care Sciences, University of Lincoln, Lincoln, UK., Smith MD; Aberystwyth Business School, Aberystwyth University, Ceredigion, UK., Ørner R; Community and Health Research Unit, School of Health and Care Sciences, University of Lincoln, Lincoln, UK., Pattinson J; Community and Health Research Unit, School of Health and Care Sciences, University of Lincoln, Lincoln, UK., Asghar Z; Community and Health Research Unit, School of Health and Care Sciences, University of Lincoln, Lincoln, UK., Rowan E; Community and Health Research Unit, School of Health and Care Sciences, University of Lincoln, Lincoln, UK., Spaight R; East Midlands Ambulance Service NHS Trust, Nottingham, UK., Mortimer C; South East Coast Ambulance Service NHS Foundation Trust, Sussex, UK., Brewster A; Community and Health Research Unit, School of Health and Care Sciences, University of Lincoln, Lincoln, UK., Mountain P; Community and Health Research Unit, School of Health and Care Sciences, University of Lincoln, Lincoln, UK., Miller J; West Midlands Ambulance Service NHS Trust, Birmingham, UK., Brown M; South Central Ambulance Service NHS Foundation Trust, Bicester, UK., Siriwardena AN; Community and Health Research Unit, School of Health and Care Sciences, University of Lincoln, Lincoln, UK. nsiriwardena@lincoln.ac.uk.

Publikováno v: Scandinavian journal of trauma, resuscitation and emergency medicine [Scand J Trauma Resusc Emerg Med] 2024 Sep 30; Vol. 32 (1), pp. 99. Date of Electronic Publication: 2024 Sep 30.

Deep Learning for Autism Diagnosis and Facial Analysis in Children.

Autor: Hosseini MP; Department of Bioengineering, Santa Clara University, Santa Clara, CA, United States.; AI Research, San Jose, CA, United States., Beary M; Department of Bioengineering, Santa Clara University, Santa Clara, CA, United States., Hadsell A; Department of Bioengineering, Santa Clara University, Santa Clara, CA, United States., Messersmith R; Department of Bioengineering, Santa Clara University, Santa Clara, CA, United States., Soltanian-Zadeh H; CIPCE, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran.

Publikováno v: Frontiers in computational neuroscience [Front Comput Neurosci] 2022 Jan 20; Vol. 15, pp. 789998. Date of Electronic Publication: 2022 Jan 20 (Print Publication: 2021).

Multimodal data analysis of epileptic EEG and rs-fMRI via deep learning and edge computing.

Autor: Hosseini MP; Department of Electrical and Computer Engineering, Rutgers University, NJ, USA; Department of Bioengineering, Santa Clara University, CA, USA; AI Research, Silicon Valley, CA, USA. Electronic address: parsa@cac.rutgers.edu., Tran TX; Department of Electrical and Computer Engineering, Rutgers University, NJ, USA., Pompili D; Department of Electrical and Computer Engineering, Rutgers University, NJ, USA., Elisevich K; Division of Neurosurgery, College of Human Medicine, Michigan State University, Grand Rapids, MI, USA; Dept. of Clinical Neurosciences, Spectrum Health, Grand Rapids, MI, USA., Soltanian-Zadeh H; CIPCE, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran; Image Analysis Lab, Depts. of Radiology and Research Administration, Henry Ford Health System, MI, USA.

Publikováno v: Artificial intelligence in medicine [Artif Intell Med] 2020 Apr; Vol. 104, pp. 101813. Date of Electronic Publication: 2020 Feb 19.

Comparative performance evaluation of automated segmentation methods of hippocampus from magnetic resonance images of temporal lobe epilepsy patients.

Autor: Hosseini MP; Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, New Jersey 08854 and Medical Image Analysis Laboratory, Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, Michigan 48202., Nazem-Zadeh MR; Medical Image Analysis Laboratory, Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, Michigan 48202., Pompili D; Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, New Jersey 08854., Jafari-Khouzani K; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02129., Elisevich K; Department of Clinical Neuroscience, Spectrum Health System, Grand Rapids, Michigan 49503 and Division of Neurosurgery, College of Human Medicine, Michigan State University, Grand Rapids, Michigan 49503., Soltanian-Zadeh H; Medical Image Analysis Laboratory, Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, Michigan 48202; Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, University of Tehran, Tehran 1439957131, Iran; and School of Cognitive Sciences, Institute for Studies in Theoretical Physics and Mathematics (IPM), Tehran 1954856316, Iran.

Publikováno v: Medical physics [Med Phys] 2016 Jan; Vol. 43 (1), pp. 538.

Support Vector Machine with nonlinear-kernel optimization for lateralization of epileptogenic hippocampus in MR images.

Autor: Hosseini MP, Nazem-Zadeh MR, Mahmoudi F, Ying H, Soltanian-Zadeh H

Publikováno v: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2014; Vol. 2014, pp. 1047-50.