Feasibility of deep learning algorithm in diagnosing lumbar central canal stenosis using abdominal CT.
| Publicado en: | Skeletal Radiology Vol. 54; no. 5; pp. 947 - 958 |
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| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
May2025
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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=184167829&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184167829 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03642348 O14 jtl: Skeletal Radiology issn: 03642348 maglogo: N pubinfo: dt: May2025 vid: 54 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184167829 179525069 10.1007/s00256-024-04796-z 184167829 ppf: 947 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Feasibility of deep learning algorithm in diagnosing lumbar central canal stenosis using abdominal CT. aug: au: Jeon, Yejin Kim, Bo Ram Choi, Hyoung In Lee, Eugene Kim, Da-Wit Choi, Boorym Lee, Joon Woo affil: https://ror.org/00cb3km46 Department of Radiology, Seoul National University Bundang Hospital, 82 Gumi-ro, 173 Beon-Gil, Bundang-Gu, 13620, Seongnam-Si, Gyeonggi-Do, Republic of Korea sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
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