Using DICOM Metadata for Radiological Image Series Categorization: a Feasibility Study on Large Clinical Brain MRI Datasets.
The growing interest in machine learning (ML) in healthcare is driven by the promise of improved patient care. However, how many ML algorithms are currently being used in clinical practice? While the technology is present, as demonstrated in a variety of commercial products, clinical integration is...
| Publicado en: | Journal of Digital Imaging Vol. 33; no. 3; pp. 747 - 763 |
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| Autores principales: | , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2020
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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=143476531&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143476531 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2020 vid: 33 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 143476531 143476531 143476531 10.1007/s10278-019-00308-x 143476531 ppf: 747 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Using DICOM Metadata for Radiological Image Series Categorization: a Feasibility Study on Large Clinical Brain MRI Datasets. aug: au: Gauriau, Romane Bridge, Christopher Chen, Lina Kitamura, Felipe Tenenholtz, Neil A. Kirsch, John E. Andriole, Katherine P. Michalski, Mark H. Bizzo, Bernardo C. affil: MGH & BWH Center for Clinical Data Science, Boston, MA, USA sug: subj: DICOM Magnetic Resonance Imaging Methods Diagnosis, Brain Methods Algorithms Image Processing, Computer Assisted Methods Automation Human Pilot Studies Machine Learning Workflow ab: The growing interest in machine learning (ML) in healthcare is driven by the promise of improved patient care. However, how many ML algorithms are currently being used in clinical practice? While the technology is present, as demonstrated in a variety of commercial products, clinical integration is hampered by a lack of infrastructure, processes, and tools. In particular, automating the selection of relevant series for a particular algorithm remains challenging. In this work, we propose a methodology to automate the identification of brain MRI sequences so that we can automatically route the relevant inputs for further image-related algorithms. The method relies on metadata required by the Digital Imaging and Communications in Medicine (DICOM) standard, resulting in generalizability and high efficiency (less than 0.4 ms/series). To support our claims, we test our approach on two large brain MRI datasets (40,000 studies in total) from two different institutions on two different continents. We demonstrate high levels of accuracy (ranging from 97.4 to 99.96%) and generalizability across the institutions. Given the complexity and variability of brain MRI protocols, we are confident that similar techniques could be applied to other forms of radiological imaging. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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