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Demonstrating an Academic Core Facility for Automated Medical Image Processing and Analysis: Workflow Design and Practical Applications.

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Název: Demonstrating an Academic Core Facility for Automated Medical Image Processing and Analysis: Workflow Design and Practical Applications.
Autoři: Kumar, Yogesh, Cardan, Rex A., Chang, Ho-hsin, Heinzman, Katherine A., Gultekin, Kadir, Goss, Amy, McDonald, Andrew, Murdaugh, Donna, McConathy, Jonathan, Rothenberg, Steven, Smith, Andrew D., Fiveash, John, Cardenas, Carlos E.
Zdroj: Diagnostics (2075-4418); Apr2025, Vol. 15 Issue 7, p803, 16p
Témata: COMPUTER-assisted image analysis (Medicine), IMAGE analysis, IMAGE processing, DIAGNOSTIC imaging, ARTIFICIAL intelligence
Abstrakt: Background/Objectives: Medical research institutions are increasingly leveraging artificial intelligence (AI) to enhance the processing and analysis of medical imaging data. However, scaling AI-driven medical image analysis often requires specialized expertise and infrastructure that individual labs may lack. A centralized solution is to establish a core facility—a shared institutional resource—dedicated to Automated Medical Image Processing and Analysis (AMIPA). Methods: This technical note offers a practical roadmap for institutions to create an AI-based core facility for AMIPA, drawing on our experience in building such a resource. Results: We outline the key components for replicating a successful AMIPA core facility, including high-performance computing resources, robust AI software pipelines, data management strategies, and dedicated support personnel. Emphasis is placed on workflow automation and reproducibility, ensuring researchers can efficiently and consistently process large imaging datasets. Conclusions: By following this roadmap, institutions can accelerate AI adoption in imaging workflows and foster a shared resource that enhances the quality and productivity of medical imaging research. [ABSTRACT FROM AUTHOR]
Copyright of Diagnostics (2075-4418) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Databáze: Biomedical Index
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  Data: Demonstrating an Academic Core Facility for Automated Medical Image Processing and Analysis: Workflow Design and Practical Applications.
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  Data: Diagnostics (2075-4418); Apr2025, Vol. 15 Issue 7, p803, 16p
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  Data: <searchLink fieldCode="DE" term="%22COMPUTER-assisted+image+analysis+%28Medicine%29%22">COMPUTER-assisted image analysis (Medicine)</searchLink><br /><searchLink fieldCode="DE" term="%22IMAGE+analysis%22">IMAGE analysis</searchLink><br /><searchLink fieldCode="DE" term="%22IMAGE+processing%22">IMAGE processing</searchLink><br /><searchLink fieldCode="DE" term="%22DIAGNOSTIC+imaging%22">DIAGNOSTIC imaging</searchLink><br /><searchLink fieldCode="DE" term="%22ARTIFICIAL+intelligence%22">ARTIFICIAL intelligence</searchLink>
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  Data: Background/Objectives: Medical research institutions are increasingly leveraging artificial intelligence (AI) to enhance the processing and analysis of medical imaging data. However, scaling AI-driven medical image analysis often requires specialized expertise and infrastructure that individual labs may lack. A centralized solution is to establish a core facility—a shared institutional resource—dedicated to Automated Medical Image Processing and Analysis (AMIPA). Methods: This technical note offers a practical roadmap for institutions to create an AI-based core facility for AMIPA, drawing on our experience in building such a resource. Results: We outline the key components for replicating a successful AMIPA core facility, including high-performance computing resources, robust AI software pipelines, data management strategies, and dedicated support personnel. Emphasis is placed on workflow automation and reproducibility, ensuring researchers can efficiently and consistently process large imaging datasets. Conclusions: By following this roadmap, institutions can accelerate AI adoption in imaging workflows and foster a shared resource that enhances the quality and productivity of medical imaging research. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Diagnostics (2075-4418) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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