reference/34808432

http://rdf.ncbi.nlm.nih.gov/pubchem/reference/34808432

Outgoing Links

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contentType	Multicenter Study\|Journal Article
issn	0196-0709
issueIdentifier	2
pageRange	103695-
publicationName	American Journal of Otolaryngology
startingPage	103695
hasFundingAgency	http://rdf.ncbi.nlm.nih.gov/pubchem/organization/MD5_c04fd0c1eda847c5e722e761bd859dc8
bibliographicCitation	Zhu JQ, Wang ML, Li Y, Zhang W, Li LJ, Liu L, Zhang Y, Han CJ, Tie CW, Wang SX, Wang GQ, Ni XG. Convolutional neural network based anatomical site identification for laryngoscopy quality control: A multicenter study. Am J Otolaryngol. 2023 Mar;44(2):103695. doi: 10.1016/j.amjoto.2022.103695. PMID: 36473265.
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date	202303
identifier	https://pubmed.ncbi.nlm.nih.gov/36473265 https://doi.org/10.1016/j.amjoto.2022.103695
isPartOf	http://rdf.ncbi.nlm.nih.gov/pubchem/journal/350 https://portal.issn.org/resource/ISSN/0196-0709
language	English
source	https://pubmed.ncbi.nlm.nih.gov/ https://www.crossref.org/
title	Convolutional neural network based anatomical site identification for laryngoscopy quality control: A multicenter study
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hasSubjectTerm	http://id.nlm.nih.gov/mesh/D016571 http://id.nlm.nih.gov/mesh/D006801 http://id.nlm.nih.gov/mesh/D007828Q000379 http://id.nlm.nih.gov/mesh/D012189
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