Machine-learning model based on ultrasomics for non-invasive evaluation of fibrosis in IgA nephropathy.
| Publicado en: | European Radiology Vol. 35; no. 7; pp. 3707 - 3721 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jul2025
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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=185942247&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185942247 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Jul2025 vid: 35 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 185942247 182385581 10.1007/s00330-025-11368-9 185942247 ppf: 3707 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Machine-learning model based on ultrasomics for non-invasive evaluation of fibrosis in IgA nephropathy. aug: au: Huang, Qun Huang, Fangyi Chen, Chengcai Xiao, Pan Liu, Jiali Gao, Yong affil: https://ror.org/030sc3x20 Department of Ultrasound, First Affiliated Hospital of Guangxi Medical University, Nanning, China sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
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