Keyword search (4,163 papers available)

"machine learning" Keyword-tagged Publications:

Title Authors PubMed ID
1 Sagittal abdominal diameter and abdominal aortic calcification are associated with incident major adverse cardiovascular events: The Manitoba Bone Density Registry Abraha HN; Gebre AK; Sim M; Smith C; Gilani SZ; Ilyas Z; Zarzour F; Schousboe JT; Lix LM; Binkley N; Reid S; Monchka BA; Kimelman D; Lewis JR; Leslie WD; 41903786
ENCS
2 Assessment of PlanetScope Spectral Data for Estimation of Peanut Leaf Area Index Using Machine Learning and Statistical Methods Ekwe M; Fernando H; James G; Adeluyi O; Verrelst J; Kross A; 41682534
CONCORDIA
3 Smart Optogenetics for Real-Time Automated Control of Cardiac Electrical Activity Deng S; Harlaar N; Zhang J; Dekker SO; Kudryashova NN; Zhou H; Bart CI; Jin T; Derevyanko G; van Driel W; Panfilov AV; Poelma RH; de Vries AAF; Zhang G; De Coster T; Pijnappels DA; 41684280
CHEMBIOCHEM
4 Towards smart PFAS management: Integrating artificial intelligence in water and wastewater systems Yaghoobian S; An J; Jeong DW; Hwang JH; 41483514
ENCS
5 New spectral indices for identifying large plastic accumulations in coastal waters with sentinel-2 imagery Wu C; Chen Z; Peng C; An C; 41406508
ENCS
6 Advancements in Magnetorheological Foams: Composition, Fabrication, AI-Driven Enhancements and Emerging Applications Khodaverdi H; Sedaghati R; 40732777
ENCS
7 Evolution from the physical process-based approaches to machine learning approaches to predicting urban floods: a literature review Md Shike Bin Mazid Anik 40692624
ENCS
8 Inferring concussion history in athletes using pose and ground reaction force estimation and stability analysis of plyometric exercise videos Alves W; Babouras A; Martineau PA; Schutt D; Robbins S; Fevens T; 40632382
ENCS
9 Statistical or Embodied? Comparing Colorseeing, Colorblind, Painters, and Large Language Models in Their Processing of Color Metaphors Nadler EO; Guilbeault D; Ringold SM; Williamson TR; Bellemare-Pepin A; Com?a IM; Jerbi K; Narayanan S; Aziz-Zadeh L; 40621800
PSYCHOLOGY
10 Application of machine learning for predicting the incubation period of water droplet erosion in metals AlHammad K; Medraj M; Tembely M; 40612685
ENCS
11 Machine learning innovations in CPR: a comprehensive survey on enhanced resuscitation techniques Islam S; Rjoub G; Elmekki H; Bentahar J; Pedrycz W; Cohen R; 40336660
ENCS
12 Clustering and Interpretability of Residential Electricity Demand Profiles Kallel S; Amayri M; Bouguila N; 40218540
ENCS
13 Automated abdominal aortic calcification scoring from vertebral fracture assessment images and fall-associated hospitalisations: the Manitoba Bone Mineral Density Registry Sim M; Gebre AK; Dalla Via J; Reid S; Jozani MJ; Kimelman D; Monchka BA; Gilani SZ; Ilyas Z; Smith C; Suter D; Schousboe JT; Lewis JR; Leslie WD; 40080298
ENCS
14 Metrics for evaluation of automatic epileptogenic zone localization in intracranial electrophysiology Hrtonova V; Nejedly P; Travnicek V; Cimbalnik J; Matouskova B; Pail M; Peter-Derex L; Grova C; Gotman J; Halamek J; Jurak P; Brazdil M; Klimes P; Frauscher B; 39608298
SOH
15 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
16 Modelling reindeer rut activity using on-animal acoustic recorders and machine learning Boucher AJ; Weladji RB; Holand Ø; Kumpula J; 38932958
BIOLOGY
17 Post-reinforcement pauses during slot machine gambling are moderated by immersion W Spencer Murch 38429228
PSYCHOLOGY
18 The State of Artificial Intelligence in Skin Cancer Publications Joly-Chevrier M; Nguyen AX; Liang L; Lesko-Krleza M; Lefrançois P; 38323537
ENCS
19 An intelligent decision support system for groundwater supply management and electromechanical infrastructure controls Ataei P; Takhtravan A; Gheibi M; Chahkandi B; Faramarz MG; Waclawek S; Fathollahi-Fard AM; Behzadian K; 38317976
ENCS
20 Machine Learning-Assisted Short-Wave InfraRed (SWIR) Techniques for Biomedical Applications: Towards Personalized Medicine Salimi M; Roshanfar M; Tabatabaei N; Mosadegh B; 38248734
ENCS
21 Editorial: Computational systems immunovirology Zarei Ghobadi M; Teymoori-Rad M; Selvaraj G; Wei DQ; 37475870
CHEMBIOCHEM
22 Class imbalance should not throw you off balance: Choosing the right classifiers and performance metrics for brain decoding with imbalanced data Thölke P; Mantilla-Ramos YJ; Abdelhedi H; Maschke C; Dehgan A; Harel Y; Kemtur A; Mekki Berrada L; Sahraoui M; Young T; Bellemare Pépin A; El Khantour C; Landry M; Pascarella A; Hadid V; Combrisson E; O' Byrne J; Jerbi K; 37385392
IMAGING
23 Prospects of Novel and Repurposed Immunomodulatory Drugs against Acute Respiratory Distress Syndrome (ARDS) Associated with COVID-19 Disease Nayak SS; Naidu A; Sudhakaran SL; Vino S; Selvaraj G; 37109050
CHEMBIOCHEM
24 Comparison of photocatalysis and photolysis of 2,2,4,4-tetrabromodiphenyl ether (BDE-47): Operational parameters, kinetic studies, and data validation using three modern machine learning models Motamedi M; Yerushalmi L; Haghighat F; Chen Z; Zhuang Y; 36907486
ENCS
25 Using machine learning to retrospectively predict self-reported gambling problems in Quebec Murch WS; Kairouz S; Dauphinais S; Picard E; Costes JM; French M; 36880253
SOCANTH
26 Unique Photoactivated Time-Resolved Response in 2D GeS for Selective Detection of Volatile Organic Compounds Mohammadzadeh MR; Hasani A; Jaferzadeh K; Fawzy M; De Silva T; Abnavi A; Ahmadi R; Ghanbari H; Askar A; Kabir F; Rajapakse RKND; Adachi MM; 36658730
PHYSICS
27 Optimizing Biodegradable Starch-Based Composite Films Formulation for Wound-Dressing Applications Delavari MM; Ocampo I; Stiharu I; 36557445
ENCS
28 Impact from the evolution of private vehicle fleet composition on traffic related emissions in the small-medium automotive city Tian X; Huang G; Song Z; An C; Chen Z; 35709991
ENCS
29 Weakly Supervised Occupancy Prediction Using Training Data Collected via Interactive Learning Bouhamed O; Amayri M; Bouguila N; 35590880
ENCS
30 Maternal exposure to black carbon and nitrogen dioxide during pregnancy and birth weight: Using machine-learning methods to achieve balance in inverse-probability weights Dong S; Abu-Awad Y; Kosheleva A; Fong KC; Koutrakis P; Schwartz JD; 35227679
PSYCHOLOGY
31 On the Impact of Biceps Muscle Fatigue in Human Activity Recognition. Elshafei M, Costa DE, Shihab E 33557239
ENCS
32 Towards Detecting Biceps Muscle Fatigue in Gym Activity Using Wearables. Elshafei M, Shihab E 33498702
ENCS
33 Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec. Khalilpourazari S, Hashemi Doulabi H 33424076
ENCS
34 Integrative approach for detecting membrane proteins. Alballa M, Butler G 33349234
CSFG
35 Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing. Ebadi A; Xi P; Tremblay S; Spencer B; Pall R; Wong A; 33230352
ENCS
36 Osseointegration Pharmacology: A Systematic Mapping Using Artificial Intelligence Mahri M; Shen N; Berrizbeitia F; Rodan R; Daer A; Faigan M; Taqi D; Wu KY; Ahmadi M; Ducret M; Emami E; Tamimi F; 33181361
CONCORDIA
37 Hybrid multi-mode machine learning-based fault diagnosis strategies with application to aircraft gas turbine engines. Shen Y, Khorasani K 32673847
ENCS

 

Title:MuscleMap: An Open-Source, Community-Supported Consortium for Whole-Body Quantitative MRI of Muscle
Authors:McKay MJWeber KAWesselink EOSmith ZAAbbott RAnderson DBAshton-James CEAtyeo JBeach AJBurns JClarke SCollins NJCoppieters MWCornwall JCrawford RJDe Martino EDunn AGEyles JPFeng HJFortin MFranettovich Smith MMGalloway GGandomkar ZGlastras SHenderson LAHides JAHiller CEHilmer SNHoggarth MAKim BLal NLaPorta LMagnussen JSMaloney SMarch LNackley AGO'Leary SPPeolsson APerraton ZPool-Goudzwaard ALSchnitzler MSeitz ALSemciw AISheard PWSmith ACSnodgrass SJSullivan JTran VValentin SWalton DMWishart LRElliott JM
Link:https://pubmed.ncbi.nlm.nih.gov/39590726/
DOI:10.3390/jimaging10110262
Publication:Journal of imaging
Keywords:MR imagingartificial intelligencemachine learningmuscle fat infiltrationneural networksnormative reference datapublic 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.





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