A DICOM Framework for Machine Learning and Processing Pipelines Against Real-time Radiology Images.
Real-time execution of machine learning (ML) pipelines on radiology images is difficult due to limited computing resources in clinical environments, whereas running them in research clusters requires efficient data transfer capabilities. We developed Niffler, an open-source Digital Imaging and Commu...
| Publicado en: | Journal of Digital Imaging Vol. 34; no. 4; pp. 1005 - 1014 |
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| Autores principales: | , , , , , , , , , |
| Formato: | diagnostic images tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2021
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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=152559516&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152559516 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2021 vid: 34 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 152559516 151946843 152559516 152559516 10.1007/s10278-021-00491-w 152559516 ppf: 1005 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A DICOM Framework for Machine Learning and Processing Pipelines Against Real-time Radiology Images. aug: au: Kathiravelu, Pradeeban Sharma, Puneet Sharma, Ashish Banerjee, Imon Trivedi, Hari Purkayastha, Saptarshi Sinha, Priyanshu Cadrin-Chenevert, Alexandre Safdar, Nabile Gichoya, Judy Wawira affil: Emory University, 30306, Atlanta, GA, USA sug: subj: DICOM Machine Learning Methods Diagnostic Imaging Methods Scanners Information Retrieval Magnetic Resonance Imaging Data Warehouse ab: Real-time execution of machine learning (ML) pipelines on radiology images is difficult due to limited computing resources in clinical environments, whereas running them in research clusters requires efficient data transfer capabilities. We developed Niffler, an open-source Digital Imaging and Communications in Medicine (DICOM) framework that enables ML and processing pipelines in research clusters by efficiently retrieving images from the hospitals' PACS and extracting the metadata from the images. We deployed Niffler at our institution (Emory Healthcare, the largest healthcare network in the state of Georgia) and retrieved data from 715 scanners spanning 12 sites, up to 350 GB/day continuously in real-time as a DICOM data stream over the past 2 years. We also used Niffler to retrieve images bulk on-demand based on user-provided filters to facilitate several research projects. This paper presents the architecture and three such use cases of Niffler. First, we executed an IVC filter detection and segmentation pipeline on abdominal radiographs in real-time, which was able to classify 989 test images with an accuracy of 96.0%. Second, we applied the Niffler Metadata Extractor to understand the operational efficiency of individual MRI systems based on calculated metrics. We benchmarked the accuracy of the calculated exam time windows by comparing Niffler against the Clinical Data Warehouse (CDW). Niffler accurately identified the scanners' examination timeframes and idling times, whereas CDW falsely depicted several exam overlaps due to human errors. Third, with metadata extracted from the images by Niffler, we identified scanners with misconfigured time and reconfigured five scanners. Our evaluations highlight how Niffler enables real-time ML and processing pipelines in a research cluster. pubtype: Academic Journal doctype: diagnostic images tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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