  Executing machine learning (ML) pipelines in real-time on radiology images is
hard due to the limited computing resources in clinical environments and the
lack of efficient data transfer capabilities to run them on research clusters.
We propose Niffler, an integrated framework that enables the execution of ML
pipelines at research clusters by efficiently querying and retrieving radiology
images from the Picture Archiving and Communication Systems (PACS) of the
hospitals. Niffler uses the Digital Imaging and Communications in Medicine
(DICOM) protocol to fetch and store imaging data and provides metadata
extraction capabilities and Application programming interfaces (APIs) to apply
filters on the images. Niffler further enables the sharing of the outcomes from
the ML pipelines in a de-identified manner. Niffler has been running stable for
more than 19 months and has supported several research projects at the
department. In this paper, we present its architecture and three of its use
cases: an inferior vena cava (IVC) filter detection from the images in
real-time, identification of scanner utilization, and scanner clock
calibration. Evaluations on the Niffler prototype highlight its feasibility and
efficiency in facilitating the ML pipelines on the images and metadata in
real-time and retrospectively.
