#5548. A DICOM Framework for Machine Learning and Processing Pipelines Against Real-time Radiology Images

August 2026publication date
Proposal available till 21-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Radiology, Nuclear Medicine and Imaging;
Radiological and Ultrasound Technology;
Computer Science Applications;
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Abstract:
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.
Keywords:
Clinical data warehouse (CDW); Digital Imaging and Communications in Medicine (DICOM); Machine learning (ML); Picture archiving and communication system (PACS)

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