Deep learning for image analysis: Personalizing medicine closer to the point of care.
The precision-based revolution in medicine continues to demand stratification of patients into smaller and more personalized subgroups. While genomic technologies have largely led this movement, diagnostic results can take days to weeks to generate. Management at, or closer to, the point of care sti...
| Publicado en: | Critical Reviews in Clinical Laboratory Sciences Vol. 56; no. 1; pp. 61 - 74 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images pictorial review tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Jan2019
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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=134622125&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 134622125 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10408363 1AV jtl: Critical Reviews in Clinical Laboratory Sciences issn: 10408363 maglogo: Y pubinfo: dt: Jan2019 vid: 56 iid: 1 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 134622125 134622125 134622125 10.1080/10408363.2018.1536111 134622125 ppf: 61 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep learning for image analysis: Personalizing medicine closer to the point of care. aug: au: Xie, Quin Faust, Kevin Van Ommeren, Randy Sheikh, Adeel Djuric, Ugljesa Diamandis, Phedias affil: Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada sug: subj: Deep Learning Individualized Medicine Diagnostic Imaging Clinical Information Systems Neural Networks (Computer) Decision Support Techniques ab: The precision-based revolution in medicine continues to demand stratification of patients into smaller and more personalized subgroups. While genomic technologies have largely led this movement, diagnostic results can take days to weeks to generate. Management at, or closer to, the point of care still heavily relies on the subjective qualitative interpretation of clinical and diagnostic imaging findings. New and emerging technological advances in artificial intelligence (AI) now appear poised to help bring objectivity and precision to these traditionally qualitative analytic tools. In particular, one specific form of AI, known as deep learning, is achieving expert-level disease classifications in many areas of diagnostic medicine dependent on visual and image-based findings. Here, we briefly review concepts of deep learning, and more specifically recent developments in convolutional neural networks (CNNs), to highlight their transformative potential in personalized medicine and, in particular, diagnostic histopathology. Understanding the opportunities and challenges of these quantitative machine-based decision support tools is critical to their widespread introduction into routine diagnostics. pubtype: Academic Journal doctype: diagnostic images pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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