Why imaging data alone is not enough: AI-based integration of imaging, omics, and clinical data.
Artificial intelligence (AI) is currently regaining enormous interest due to the success of machine learning (ML), and in particular deep learning (DL). Image analysis, and thus radiomics, strongly benefits from this research. However, effectively and efficiently integrating diverse clinical, imagin...
| Published in: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 46; no. 13; pp. 2722 - 2731 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Dec2019
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=139867257&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 139867257 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Dec2019 vid: 46 iid: 13 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 139867257 144026312 10.1007/s00259-019-04382-9 139867257 ppf: 2722 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Why imaging data alone is not enough: AI-based integration of imaging, omics, and clinical data. aug: au: Holzinger, Andreas Haibe-Kains, Benjamin Jurisica, Igor affil: Institute for Medical Informatics / Statistics, Medical University Graz, Auenbruggerplatz 2/V, 8036, Graz, Austria sug: ab: Artificial intelligence (AI) is currently regaining enormous interest due to the success of machine learning (ML), and in particular deep learning (DL). Image analysis, and thus radiomics, strongly benefits from this research. However, effectively and efficiently integrating diverse clinical, imaging, and molecular profile data is necessary to understand complex diseases, and to achieve accurate diagnosis in order to provide the best possible treatment. In addition to the need for sufficient computing resources, suitable algorithms, models, and data infrastructure, three important aspects are often neglected: (1) the need for multiple independent, sufficiently large and, above all, high-quality data sets; (2) the need for domain knowledge and ontologies; and (3) the requirement for multiple networks that provide relevant relationships among biological entities. While one will always get results out of high-dimensional data, all three aspects are essential to provide robust training and validation of ML models, to provide explainable hypotheses and results, and to achieve the necessary trust in AI and confidence for clinical applications. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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