Deep learning approach to predict pain progression in knee osteoarthritis.
| Publicado en: | Skeletal Radiology Vol. 51; no. 2; pp. 363 - 374 |
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| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Feb2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=154247777&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154247777 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03642348 O14 jtl: Skeletal Radiology issn: 03642348 maglogo: N pubinfo: dt: Feb2022 vid: 51 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 154247777 149726584 10.1007/s00256-021-03773-0 154247777 ppf: 363 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep learning approach to predict pain progression in knee osteoarthritis. aug: au: Guan, Bochen Liu, Fang Mizaian, Arya Haj Demehri, Shadpour Samsonov, Alexey Guermazi, Ali Kijowski, Richard affil: Department of Radiology, University of Wisconsin, 1111 Highland Avenue, 53705-2275, Madison, WI, USA sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
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