Development of Medical Imaging Data Standardization for Imaging-Based Observational Research: OMOP Common Data Model Extension

Park, Woo Yeon; Jeon, Kyulee; Schmidt, Teri Sippel; Kondylakis, Haridimos; Alkasab, Tarik; Dewey, Blake E.; You, Seng Chan; Nagy, Paul

doi:10.1007/s10278-024-00982-6

Cited by 7 publications

References 14 publications

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Advancing Medical Imaging Research Through Standardization

Jeon,

Park,

Kahn

et al. 2024

Invest Radiol

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Artificial intelligence (AI) has made significant advances in radiology. Nonetheless, challenges in AI development, validation, and reproducibility persist, primarily due to the lack of high-quality, large-scale, standardized data across the world. Addressing these challenges requires comprehensive standardization of medical imaging data and seamless integration with structured medical data. Developed by the Observational Health Data Sciences and Informatics community, the OMOP Common Data Model enables large-scale international collaborations with structured medical data. It ensures syntactic and semantic interoperability, while supporting the privacy-protected distribution of research across borders. The recently proposed Medical Imaging Common Data Model is designed to encompass all DICOM-formatted medical imaging data and integrate imaging-derived features with clinical data, ensuring their provenance. The harmonization of medical imaging data and its seamless integration with structured clinical data at a global scale will pave the way for advanced AI research in radiology. This standardization will enable federated learning, ensuring privacy-preserving collaboration across institutions and promoting equitable AI through the inclusion of diverse patient populations. Moreover, it will facilitate the development of foundation models trained on large-scale, multimodal datasets, serving as powerful starting points for specialized AI applications. Objective and transparent algorithm validation on a standardized data infrastructure will enhance reproducibility and interoperability of AI systems, driving innovation and reliability in clinical applications.

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Advancing Medical Imaging Research Through Standardization

Jeon,

Park,

Kahn

et al. 2024

Invest Radiol

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Documenting the de-identification process of clinical and imaging data for AI for health imaging projects

Kondylakis,

Catalan,

Alabart

et al. 2024

Insights Imaging

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Artificial intelligence (AI) is revolutionizing the field of medical imaging, holding the potential to shift medicine from a reactive “sick-care” approach to a proactive focus on healthcare and prevention. The successful development of AI in this domain relies on access to large, comprehensive, and standardized real-world datasets that accurately represent diverse populations and diseases. However, images and data are sensitive, and as such, before using them in any way the data needs to be modified to protect the privacy of the patients. This paper explores the approaches in the domain of five EU projects working on the creation of ethically compliant and GDPR-regulated European medical imaging platforms, focused on cancer-related data. It presents the individual approaches to the de-identification of imaging data, and describes the problems and the solutions adopted in each case. Further, lessons learned are provided, enabling future projects to optimally handle the problem of data de-identification. Critical relevance statement This paper presents key approaches from five flagship EU projects for the de-identification of imaging and clinical data offering valuable insights and guidelines in the domain. Key Points ΑΙ models for health imaging require access to large amounts of data. Access to large imaging datasets requires an appropriate de-identification process. This paper provides de-identification guidelines from the AI for health imaging (AI4HI) projects.

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Integrating Clinical Data and Medical Imaging in Lung Cancer: Feasibility Study Using the Observational Medical Outcomes Partnership Common Data Model Extension

Ji,

Kim,

Sunwoo

et al. 2024

JMIR Med Inform

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Background: Digital transformation, particularly the integration of medical imaging with clinical data, is vital in personalized medicine. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) standardizes health data. However, integrating medical imaging remains a challenge.Objective: This study proposes a method for combining medical imaging data with the OMOP CDM to improve multimodal research.

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Development of Medical Imaging Data Standardization for Imaging-Based Observational Research: OMOP Common Data Model Extension

Cited by 7 publications

References 14 publications

Advancing Medical Imaging Research Through Standardization

Advancing Medical Imaging Research Through Standardization

Documenting the de-identification process of clinical and imaging data for AI for health imaging projects

Integrating Clinical Data and Medical Imaging in Lung Cancer: Feasibility Study Using the Observational Medical Outcomes Partnership Common Data Model Extension

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