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- Title
Federated Deep Learning to More Reliably Detect Body Part for Hanging Protocols, Relevant Priors, and Workflow Optimization.
- Authors
Filice, Ross W.; Stein, Anouk; Pan, Ian; Shih, George
- Abstract
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.
- Subjects
PELVIC radiography; DEEP learning; DIGITAL image processing; MEDICAL radiology; INFORMATION storage &; retrieval systems; MEDICAL databases; CHEST X rays; DICOM (Computer network protocol); ARTIFICIAL intelligence; WORKFLOW; COMPUTED tomography; ARTIFICIAL neural networks; DIGITAL diagnostic imaging; ABDOMINAL radiography; ALGORITHMS
- Publication
Journal of Digital Imaging, 2022, Vol 35, Issue 2, p335
- ISSN
0897-1889
- Publication type
Article
- DOI
10.1007/s10278-021-00547-x