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Federated Deep Learning to More Reliably Detect Body Part for Hanging Protocols, Relevant Priors, and Workflow Optimization

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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.

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Availability of Data and Material

The source data contains PHI and is not available for public consumption.

Code Availability

The underlying model has been submitted to the 2021 SIIM Annual Meeting for evaluation. If it is made publicly available, we will be happy to support that.

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Acknowledgements

We acknowledge the support of Nvidia Corporation with the donation of a Quadro P6000 via an academic GPU grant which was used for this research.

Funding

An Nvidia Quadro P6000 graphics card (estimated value $5000) was donated by Nvidia Corporation as part of a broad academic grant and was used for this work.

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Authors and Affiliations

  1. Department of Radiology, MedStar Georgetown University Hospital, 3800 Reservoir Road, NW CG201, Washington DC, 20007, USA

    Ross W. Filice

  2. Department of Medical Imaging, University of Toronto, 151 College St, Toronto, ON, M5S 3E2, Canada

    Anouk Stein

  3. Department of Radiology, Weill Cornell Medicine, 525 E. 68th St, New York, NY, 10065, USA

    George Shih

  4. Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis St, Boston, MA, 02115, USA

    Ian Pan

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  1. Ross W. Filice
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  2. Anouk Stein
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Contributions

Ross Filice participated in the design and implementation of the study and was the primary manuscript author. Anouk Stein participated in the design and implementation of the study and helped with the writing of the manuscript. Ian Pan developed the underlying infrastructure and deep learning architecture design. George Shih supervised the design and implementation. All authors reviewed the manuscript prior to submission.

Corresponding author

Correspondence to Ross W. Filice.

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Conflict of Interest

Ross Filice, M.D. received an academic GPU grant from Nvidia Corporation which supported this work. Two authors (Anouk Stein, M.D. and George Shih, M.D., M.S.) serve as stakeholders and/or consultants for MD.ai.

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Filice, R.W., Stein, A., Pan, I. et al. Federated Deep Learning to More Reliably Detect Body Part for Hanging Protocols, Relevant Priors, and Workflow Optimization. J Digit Imaging 35, 335–339 (2022). https://doi.org/10.1007/s10278-021-00547-x

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  • DOI: https://doi.org/10.1007/s10278-021-00547-x

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