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Hanging protocol optimization of lumbar spine radiographs with machine learning

Skeletal Radiology(2021)10.1007/s00256-021-03733-8Source: DataRank Database

Hanging protocol optimization of lumbar spine radiographs with machine learning is a research paper published in Skeletal Radiology (2021). On theSindex it has a DataRank of 0. It has been cited 15 times.

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Open Access15 citations

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Enrichment:Funding (2 grants)OA: greenTopics (27)IDs (PubMed, PMC)Annotations (1)

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Authors (1)

Gene Kitamura ORCID

Live enrichment

PubMed:33590305 PMC:PMC8277694

National Institutes of Health

Grant: UL1 TR001857

National Institutes of Health

Grant: 3UL1TR001857-07S1

University of Pittsburgh Clinical and Translational Science Institute

greenLicense: Springer TDM

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/8277694

publisherhttps://link.springer.com/content/pdf/10.1007/s00256-021-03733-8.pdf

publisherhttps://link.springer.com/article/10.1007/s00256-021-03733-8/fulltext.html

https://doi.org/10.1007/s00256-021-03733-8

https://pubmed.ncbi.nlm.nih.gov/33590305

Fields of Study

03 medical and health sciences0302 clinical medicineBiomechanical PhenomenaHumansLumbar VertebraeLumbosacral RegionMachine LearningRadiographyRange of Motion, Articular

MeSH Terms

Lumbosacral RegionLumbar VertebraeHumansRadiographyRange of Motion, ArticularBiomechanical PhenomenaMachine Learning

Keywords

Lumbar SpineWorkflowRadiographsMachine LearningHanging ProtocolRadiographyLumbar VertebraeLumbosacral RegionHumansRange of Motion, ArticularBiomechanical Phenomena

Hanging protocol optimization of lumbar spine radiographs with machine learning

FAIR Checklist

Authors (1)

Funding (2 grants)

Open Access

Topics

Annotations (Europe PMC)