Integration of AI lesion classification, age, and BI-RADS assessment to reduce benign biopsies on breast ultrasound.
| Publicado en: | European Radiology Vol. 35; no. 9; pp. 5658 - 5671 |
|---|---|
| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2025
|
| 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=187309338&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187309338 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Sep2025 vid: 35 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187309338 183891049 10.1007/s00330-025-11467-7 187309338 ppf: 5658 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Integration of AI lesion classification, age, and BI-RADS assessment to reduce benign biopsies on breast ultrasound. aug: au: Ju, Yan Zhang, Ge Wan, Yi Wang, Gang Shu, Rui Zhang, Panpan Song, Hongping affil: https://ror.org/00ms48f15 Department of Ultrasound, Xijing Hospital, Fourth Military Medical University, Xi'an, China sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
|---|