Federated Deep Learning to More Reliably Detect Body Part for Hanging Protocols, Relevant Priors, and Workflow Optimization.
Preparing radiology examinations for interpretation requires prefetching relevant prior examinations and implementing hanging protocols to optimally display the examination along with comparisons. Body part is a critical piece of information to facilitate both prefetching and hanging protocols, but...
| Publicado en: | Journal of Digital Imaging Vol. 35; no. 2; pp. 335 - 340 |
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| Autores principales: | , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2022
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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=155757666&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155757666 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2022 vid: 35 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 155757666 154617078 155757666 155757666 10.1007/s10278-021-00547-x 155757666 ppf: 335 ppct: 5 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Federated Deep Learning to More Reliably Detect Body Part for Hanging Protocols, Relevant Priors, and Workflow Optimization. aug: au: Filice, Ross W. Stein, Anouk Pan, Ian Shih, George affil: Department of Radiology, MedStar Georgetown University Hospital, 3800 Reservoir Road, NW CG201, 20007, Washington DC, USA sug: subj: Deep Learning Workflow DICOM Tomography, X-Ray Computed Methods Digital Imaging Methods Image Processing, Computer Assisted Radiology Information Systems Radiography, Thoracic Radiography, Abdominal Pelvis Radiography Human Artificial Intelligence Neural Networks (Computer) Algorithms ab: Preparing radiology examinations for interpretation requires prefetching relevant prior examinations and implementing hanging protocols to optimally display the examination along with comparisons. Body part is a critical piece of information to facilitate both prefetching and hanging protocols, but body part information encoded using the Digital Imaging and Communications in Medicine (DICOM) standard is widely variable, error-prone, not granular enough, or missing altogether. This results in inappropriate examinations being prefetched or relevant examinations left behind; hanging protocol optimization suffers as well. Modern artificial intelligence (AI) techniques, particularly when harnessing federated deep learning techniques, allow for highly accurate automatic detection of body part based on the image data within a radiological examination; this allows for much more reliable implementation of this categorization and workflow. Additionally, new avenues to further optimize examination viewing such as dynamic hanging protocol and image display can be implemented using these techniques. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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