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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]
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Databáze: Biomedical Index
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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]
ISSN:20754418
DOI:10.3390/diagnostics15070803