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Workflow Integration of Research AI Tools into a Hospital Radiology Rapid Prototyping Environment

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Abstract

The field of artificial intelligence (AI) in medical imaging is undergoing explosive growth, and Radiology is a prime target for innovation. The American College of Radiology Data Science Institute has identified more than 240 specific use cases where AI could be used to improve clinical practice. In this context, thousands of potential methods are developed by research labs and industry innovators. Deploying AI tools within a clinical enterprise, even on limited retrospective evaluation, is complicated by security and privacy concerns. Thus, innovation must be weighed against the substantive resources required for local clinical evaluation. To reduce barriers to AI validation while maintaining rigorous security and privacy standards, we developed the AI Imaging Incubator. The AI Imaging Incubator serves as a DICOM storage destination within a clinical enterprise where images can be directed for novel research evaluation under Institutional Review Board approval. AI Imaging Incubator is controlled by a secure HIPAA-compliant front end and provides access to a menu of AI procedures captured within network-isolated containers. Results are served via a secure website that supports research and clinical data formats. Deployment of new AI approaches within this system is streamlined through a standardized application programming interface. This manuscript presents case studies of the AI Imaging Incubator applied to randomizing lung biopsies on chest CT, liver fat assessment on abdomen CT, and brain volumetry on head MRI.

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Source: Adapted from [33, 34]

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The code used for this article is available from the corresponding author on reasonable request.

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Funding

The pilot funding for this project is hosted by VUMC under RadX Innovation Challenge. The ImageVU resource described was supported by CTSA award No. UL1 TR002243 from the National Center for Advancing Translational Sciences. The Institutional award that supported liver AI is ESS Innovation Challenge Support through VUMC Internal Funding.

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Authors and Affiliations

  1. Computer Science, Vanderbilt University, Nashville, TN, USA

    Praitayini Kanakaraj, Karthik Ramadass, Shunxing Bao, Ho Hin Lee, Kaiwen Xu, Yuankai Huo & Bennett A. Landman

  2. Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA

    Melissa Basford & Laura M. Jones

  3. Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA

    Kurt G. Schilling & Bennett A. Landman

  4. Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA

    Kurt G. Schilling, John Jeffrey Carr, James Gregory Terry, Kim Lori Sandler, Allen T. Netwon & Bennett A. Landman

  5. Electrical Engineering, Vanderbilt University, Nashville, TN, USA

    Bennett A. Landman

  6. Biomedical Engineering, Vanderbilt University, Nashville, TN, USA

    Bennett A. Landman

  7. Data Science Institute, Vanderbilt University, Nashville, TN, USA

    Yuankai Huo & Bennett A. Landman

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  1. Praitayini Kanakaraj
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Correspondence to Praitayini Kanakaraj.

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Kanakaraj, P., Ramadass, K., Bao, S. et al. Workflow Integration of Research AI Tools into a Hospital Radiology Rapid Prototyping Environment. J Digit Imaging 35, 1023–1033 (2022). https://doi.org/10.1007/s10278-022-00601-2

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