BookR: School of Health Core Facilities Booking
PubMed Publications| Keyword search (4,163 papers available) |  |
"neural networks" Keyword-tagged Publications:
| Title | Authors | PubMed ID | |
|---|---|---|---|
| 1 | Tuning Deep Learning for Predicting Aluminum Prices Under Different Sampling: Bayesian Optimization Versus Random Search | Alicia Estefania Antonio Figueroa | 41751647 CONCORDIA |
| 2 | Distinguishing Between Healthy and Unhealthy Newborns Based on Acoustic Features and Deep Learning Neural Networks Tuned by Bayesian Optimization and Random Search Algorithm | Lahmiri S; Tadj C; Gargour C; | 41294952 ENCS |
| 3 | Efficient neural encoding as revealed by bilingualism | Moore C; Donhauser PW; Klein D; Byers-Heinlein K; | 40828024 PSYCHOLOGY |
| 4 | Personalizing brain stimulation: continual learning for sleep spindle detection | Sobral M; Jourde HR; Marjani Bajestani SE; Coffey EBJ; Beltrame G; | 40609549 PSYCHOLOGY |
| 5 | Parallel boosting neural network with mutual information for day-ahead solar irradiance forecasting | Ahmed U; Mahmood A; Khan AR; Kuhlmann L; Alimgeer KS; Razzaq S; Aziz I; Hammad A; | 40185800 PHYSICS |
| 6 | Large language models deconstruct the clinical intuition behind diagnosing autism | Stanley J; Rabot E; Reddy S; Belilovsky E; Mottron L; Bzdok D; | 40147442 ENCS |
| 7 | MuscleMap: An Open-Source, Community-Supported Consortium for Whole-Body Quantitative MRI of Muscle | McKay MJ; Weber KA; Wesselink EO; Smith ZA; Abbott R; Anderson DB; Ashton-James CE; Atyeo J; Beach AJ; Burns J; Clarke S; Collins NJ; Coppieters MW; Cornwall J; Crawford RJ; De Martino E; Dunn AG; Eyles JP; Feng HJ; Fortin M; Franettovich Smith MM; Galloway G; Gandomkar Z; Glastras S; Henderson LA; Hides JA; Hiller CE; Hilmer SN; Hoggarth MA; Kim B; Lal N; LaPorta L; Magnussen JS; Maloney S; March L; Nackley AG; O' Leary SP; Peolsson A; Perraton Z; Pool-Goudzwaard AL; Schnitzler M; Seitz AL; Semciw AI; Sheard PW; Smith AC; Snodgrass SJ; Sullivan J; Tran V; Valentin S; Walton DM; Wishart LR; Elliott JM; | 39590726 HKAP |
| 8 | A protocol for trustworthy EEG decoding with neural networks | Borra D; Magosso E; Ravanelli M; | 39549492 ENCS |
| 9 | Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks | Adcock B; Brugiapaglia S; Dexter N; Moraga S; | 39454372 MATHSTATS |
| 10 | Deep neural network-based robotic visual servoing for satellite target tracking | Ghiasvand S; Xie WF; Mohebbi A; | 39440297 ENCS |
| 11 | Generalization limits of Graph Neural Networks in identity effects learning | D' Inverno GA; Brugiapaglia S; Ravanelli M; | 39426036 ENCS |
| 12 | The immunomodulatory effect of oral NaHCO3 is mediated by the splenic nerve: multivariate impact revealed by artificial neural networks | Alvarez MR; Alkaissi H; Rieger AM; Esber GR; Acosta ME; Stephenson SI; Maurice AV; Valencia LMR; Roman CA; Alarcon JM; | 38549144 CSBN |
| 13 | Reinforcement learning for automatic quadrilateral mesh generation: A soft actor-critic approach | Pan J; Huang J; Cheng G; Zeng Y; | 36375347 ENCS |
| 14 | Comparative Evaluation of Artificial Neural Networks and Data Analysis in Predicting Liposome Size in a Periodic Disturbance Micromixer | Ocampo I; López RR; Camacho-León S; Nerguizian V; Stiharu I; | 34683215 ENCS |
| 15 | X-Vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection From Speech | Jeancolas L; Petrovska-Delacrétaz D; Mangone G; Benkelfat BE; Corvol JC; Vidailhet M; Lehéricy S; Benali H; | 33679361 PERFORM |
| Title: | MuscleMap: An Open-Source, Community-Supported Consortium for Whole-Body Quantitative MRI of Muscle | ||||
| Authors: | McKay MJ, Weber KA, Wesselink EO, Smith ZA, Abbott R, Anderson DB, Ashton-James CE, Atyeo J, Beach AJ, Burns J, Clarke S, Collins NJ, Coppieters MW, Cornwall J, Crawford RJ, De Martino E, Dunn AG, Eyles JP, Feng HJ, Fortin M, Franettovich Smith MM, Galloway G, Gandomkar Z, Glastras S, Henderson LA, Hides JA, Hiller CE, Hilmer SN, Hoggarth MA, Kim B, Lal N, LaPorta L, Magnussen JS, Maloney S, March L, Nackley AG, O', Leary SP, Peolsson A, Perraton Z, Pool-Goudzwaard AL, Schnitzler M, Seitz AL, Semciw AI, Sheard PW, Smith AC, Snodgrass SJ, Sullivan J, Tran V, Valentin S, Walton DM, Wishart LR, Elliott JM | ||||
| Link: | https://pubmed.ncbi.nlm.nih.gov/39590726/ | ||||
| DOI: | 10.3390/jimaging10110262 | ||||
| Publication: | Journal of imaging | ||||
| Keywords: | MR imaging; artificial intelligence; machine learning; muscle fat infiltration; neural networks; normative reference data; public datasets; | ||||
| PMID: | 39590726 | Category: | Date Added: | 2024-11-26 | |
| Dept Affiliation: |
HKAP
1 Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia. 2 Division of Pain Medicine, Stanford University School of Medicine, Stanford University, Stanford, CA 94304, USA. 3 Faculty of Behavioural and Movement Sciences, Amsterdam Movement Sciences-Program Musculoskeletal Health, Vrije Universiteit Amsterdam, 1081 BT Amsterdam, The Netherlands. 4 Department of Rehabilitation Medicine, University of Oklahoma, Norman, OK 73019, USA. 5 Department of Rehabilitation Medicine, University of Minnesota, Minneapolis, MN 55455, USA. 6 Faculty of Medicine, Health and Human Sciences, Macquarie University, Macquarie Park, NSW 2109, Australia. 7 Disability Prevention Program, Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN 38105, USA. 8 School of Health and Rehabilitation Sciences, University of Queensland, Brisbane, 4072 QLD, Australia. 9 School of Health Sciences and Social Work, Griffith University, Brisbane, QLD 4111, Australia. 10 Otago Medical School, University of Otago, Dunedin 9016, New Zealand. 11 Faculty of Health Sciences, Curtin University, Perth, WA 6845, Australia. 12 Department of Health Science and Technology, Aalborg University, Gistrup, 9260 North Jutland, Denmark. 13 Northern Sydney Local Health District, The Kolling Institute, St Leonards, NSW 2065, Australia. 14 Department of Health, Kinesiology & Applied Physiology, Concordia University, Montreal, QC H4B 1R6, Canada. 15 Herston Imaging Research Facility, University of Queensland, Brisbane, QLD 4072, Australia. 16 Department of Physical Therapy, North Central College, Naperville, IL 60540, USA. 17 School of Rehabilitative and Health Sciences, Regis University, Denver, CO 80221, USA. 18 Center for Translational Pain Medicine, Department of Anesthesiology, School of Medicine, Duke University, Durham, NC 27710, USA. 19 Occupational and Environmental Medicine Centre, Department of Health Medicine and Caring Sciences, Unit of Clinical Medicine, Linköping University, 58183 Linköping, Sweden. 20 Department of Health Medicine and Caring Sciences, Unit of Physiotherapy, Linköping University, 58183 Linköping, Sweden. 21 School of Allied Health, La Trobe University, Melbourne, VIC 3086, Australia. 22 Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA. 23 School of Medicine, University of Colorado, Aurora, CO 80045, USA. 24 Discipline of Physiotherapy, University of Newcastle, Callaghan, NSW 2308, Australia. 25 Adelaide Medical School, University of Adelaide, Adelaide, SA 5005, Australia. 26 School of Health & Social Care, Edinburgh Napier University, Edinburgh, Scotland EH11 4BN, UK. 27 School of Physical Therapy, Western University, London, ON N6A 3K7, Canada. 28 School of Medicine and Dentistry, Griffith University, Brisbane, QLD 4111, Australia. |
||||
Description: |
Disorders affecting the neurological and musculoskeletal systems represent international health priorities. A significant impediment to progress in trials of new therapies is the absence of responsive, objective, and valid outcome measures sensitive to early disease changes. A key finding in individuals with neuromuscular and musculoskeletal disorders is the compositional changes to muscles, evinced by the expression of fatty infiltrates. Quantification of skeletal muscle composition by MRI has emerged as a sensitive marker for the severity of these disorders; however, little is known about the composition of healthy muscles across the lifespan. Knowledge of what is 'typical' age-related muscle composition is essential to accurately identify and evaluate what is 'atypical'. This innovative project, known as the MuscleMap, will achieve the first important steps towards establishing a world-first, normative reference MRI dataset of skeletal muscle composition with the potential to provide valuable insights into various diseases and disorders, ultimately improving patient care and advancing research in the field. |
