Machine learning concepts, concerns and opportunities for a pediatric radiologist.
Machine learning, a subfield of artificial intelligence, is a rapidly evolving technology that offers great potential for expanding the quality and value of pediatric radiology. We describe specific types of learning, including supervised, unsupervised and semisupervised. Subsequently, we illustrate...
| Publicado en: | Pediatric Radiology Vol. 49; no. 4; pp. 509 - 517 |
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| Autores principales: | , , , , |
| Formato: | review tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2019
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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=135608327&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135608327 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03010449 O03 jtl: Pediatric Radiology issn: 03010449 maglogo: N pubinfo: dt: Apr2019 vid: 49 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 135608327 135608327 NLM30923883 135608327 10.1007/s00247-018-4277-7 NLM30923883 135608327 ppf: 509 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Machine learning concepts, concerns and opportunities for a pediatric radiologist. aug: au: Moore, Michael M. Slonimsky, Einat Long, Aaron D. Sze, Raymond W. Iyer, Ramesh S. affil: Department of Radiology, Penn State Health, P.O. Box 850, Mail Code H066, 500 University Drive, 17033-0850, Hershey, PA, USA sug: subj: Patient Safety Quality Improvement Diagnostic Errors Prevention and Control Specialties, Medical Methods Pediatrics Methods Medical Organizations ab: Machine learning, a subfield of artificial intelligence, is a rapidly evolving technology that offers great potential for expanding the quality and value of pediatric radiology. We describe specific types of learning, including supervised, unsupervised and semisupervised. Subsequently, we illustrate two core concepts for the reader: data partitioning and under/overfitting. We also provide an expanded discussion of the challenges of implementing machine learning in children's imaging. These include the requirement for very large data sets, the need to accurately label these images with a relatively small number of pediatric imagers, technical and regulatory hurdles, as well as the opaque character of convolution neural networks. We review machine learning cases in radiology including detection, classification and segmentation. Last, three pediatric radiologists from the Society for Pediatric Radiology Quality and Safety Committee share perspectives for potential areas of development. pubtype: Academic Journal doctype: review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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