Artificial intelligence in ob/gyn ultrasound.
| Publicado en: | Contemporary OB/GYN Vol. 64; no. 10; pp. 30 - 33 |
|---|---|
| Autores principales: | , |
| Formato: | diagnostic images pictorial Journal Article |
| Publicado: |
MJH Life Sciences
Oct2019
|
| 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=139147051&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 139147051 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00903159 12O jtl: Contemporary OB/GYN issn: 00903159 maglogo: N pubinfo: dt: Oct2019 vid: 64 iid: 10 pid: 54670 pub: MJH Life Sciences place: Cranbury, New Jersey artinfo: ui: 139147051 139147051 139147051 139147051 ppf: 30 ppct: 3 formats: fmt: @attributes: type: P tig: atl: Artificial intelligence in ob/gyn ultrasound. aug: au: HAN, CHRISTINA S. DATKHAEVA, ILINA affil: Maternal-Fetal Medicine Fellowship Director at the University of California, Los Angeles sug: subj: Artificial Intelligence Ultrasonography, Prenatal Diagnosis, Computer Assisted Machine Learning Neural Networks (Computer) Image Interpretation, Computer Assisted pubtype: Academic Journal doctype: diagnostic images pictorial Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
|---|