Artificial intelligence applications for pediatric oncology imaging.
Machine learning algorithms can help to improve the accuracy and efficiency of cancer diagnosis, selection of personalized therapies and prediction of long-term outcomes. Artificial intelligence (AI) describes a subset of machine learning that can identify patterns in data and take actions to reach...
| Publicado en: | Pediatric Radiology Vol. 49; no. 11; pp. 1384 - 1391 |
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| Autor principal: | |
| Formato: | review Journal Article |
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Springer Nature
Oct2019
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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=139163716&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 139163716 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03010449 O03 jtl: Pediatric Radiology issn: 03010449 maglogo: N pubinfo: dt: Oct2019 vid: 49 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 139163716 139163716 NLM31620840 139163716 10.1007/s00247-019-04360-1 NLM31620840 139163716 ppf: 1384 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Artificial intelligence applications for pediatric oncology imaging. aug: au: Daldrup-Link, Heike affil: Department of Radiology, Lucile Packard Children's Hospital, Pediatric Molecular Imaging Program, Stanford University School of Medicine, 725 Welch Road, Room 1665, 94305-5614, Stanford, CA, USA sug: subj: Neoplasms Pediatrics Artificial Intelligence Impact of Events Scale Scales Short Portable Mental Status Questionnaire ab: Machine learning algorithms can help to improve the accuracy and efficiency of cancer diagnosis, selection of personalized therapies and prediction of long-term outcomes. Artificial intelligence (AI) describes a subset of machine learning that can identify patterns in data and take actions to reach pre-set goals without specific programming. Machine learning tools can help to identify high-risk populations, prescribe personalized screening tests and enrich patient populations that are most likely to benefit from advanced imaging tests. AI algorithms can also help to plan personalized therapies and predict the impact of genomic variations on the sensitivity of normal and tumor tissue to chemotherapy or radiation therapy. The two main bottlenecks for successful AI applications in pediatric oncology imaging to date are the needs for large data sets and appropriate computer and memory power. With appropriate data entry and processing power, deep convolutional neural networks (CNNs) can process large amounts of imaging data, clinical data and medical literature in very short periods of time and thereby accelerate literature reviews, correct diagnoses and personalized treatments. This article provides a focused review of emerging AI applications that are relevant for the pediatric oncology imaging community. pubtype: Academic Journal doctype: review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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