Exchange of Quantitative Computed Tomography Assessed Body Composition Data Using Fast Healthcare Interoperability Resources as a Necessary Step Toward Interoperable Integration of Opportunistic Screening Into Clinical Practice: Methodological Development Study
BackgroundFast Healthcare Interoperability Resources (FHIR) is a widely used standard for storing and exchanging health care data. At the same time, image-based artificial intelligence (AI) models for quantifying relevant body structures and organs from routine computed tomog...
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JMIR Publications
2025-05-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2025/1/e68750 |
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| author | Yutong Wen Vin Yeang Choo Jan Horst Eil Sylvia Thun Daniel Pinto dos Santos Johannes Kast Stefan Sigle Hans-Ulrich Prokosch Diana Lizzhaid Ovelgönne Katarzyna Borys Judith Kohnke Kamyar Arzideh Philipp Winnekens Giulia Baldini Cynthia Sabrina Schmidt Johannes Haubold Felix Nensa Obioma Pelka René Hosch |
| author_facet | Yutong Wen Vin Yeang Choo Jan Horst Eil Sylvia Thun Daniel Pinto dos Santos Johannes Kast Stefan Sigle Hans-Ulrich Prokosch Diana Lizzhaid Ovelgönne Katarzyna Borys Judith Kohnke Kamyar Arzideh Philipp Winnekens Giulia Baldini Cynthia Sabrina Schmidt Johannes Haubold Felix Nensa Obioma Pelka René Hosch |
| author_sort | Yutong Wen |
| collection | DOAJ |
| description |
BackgroundFast Healthcare Interoperability Resources (FHIR) is a widely used standard for storing and exchanging health care data. At the same time, image-based artificial intelligence (AI) models for quantifying relevant body structures and organs from routine computed tomography (CT)/magnetic resonance imaging scans have emerged. The missing link, simultaneously a needed step in advancing personalized medicine, is the incorporation of measurements delivered by AI models into an interoperable and standardized format. Incorporating image-based measurements and biomarkers into FHIR profiles can standardize data exchange, enabling timely, personalized treatment decisions and improving the precision and efficiency of patient care.
ObjectiveThis study aims to present the synergistic incorporation of CT-derived body organ and composition measurements with FHIR, delineating an initial paradigm for storing image-based biomarkers.
MethodsThis study integrated the results of the Body and Organ Analysis (BOA) model into FHIR profiles to enhance the interoperability of image-based biomarkers in radiology. The BOA model was selected as an exemplary AI model due to its ability to provide detailed body composition and organ measurements from CT scans. The FHIR profiles were developed based on 2 primary observation types: Body Composition Analysis (BCA Observation) for quantitative body composition metrics and Body Structure Observation for organ measurements. These profiles were structured to interoperate with a specially designed Diagnostic Report profile, which references the associated Imaging Study, ensuring a standardized linkage between image data and derived biomarkers. To ensure interoperability, all labels were mapped to SNOMED CT (Systematized Nomenclature of Medicine – Clinical Terms) or RadLex terminologies using specific value sets. The profiles were developed using FHIR Shorthand (FSH) and SUSHI, enabling efficient definition and implementation guide generation, ensuring consistency and maintainability.
ResultsIn this study, 4 BOA profiles, namely, Body Composition Analysis Observation, Body Structure Volume Observation, Diagnostic Report, and Imaging Study, have been presented. These FHIR profiles, which cover 104 anatomical landmarks, 8 body regions, and 8 tissues, enable the interoperable usage of the results of AI segmentation models, providing a direct link between image studies, series, and measurements.
ConclusionsThe BOA profiles provide a foundational framework for integrating AI-derived imaging biomarkers into FHIR, bridging the gap between advanced imaging analytics and standardized health care data exchange. By enabling structured, interoperable representation of body composition and organ measurements, these profiles facilitate seamless integration into clinical and research workflows, supporting improved data accessibility and interoperability. Their adaptability allows for extension to other imaging modalities and AI models, fostering a more standardized and scalable approach to using imaging biomarkers in precision medicine. This work represents a step toward enhancing the integration of AI-driven insights into digital health ecosystems, ultimately contributing to more data-driven, personalized, and efficient patient care. |
| format | Article |
| id | doaj-art-33f758932a3e46dfbd8b2e52a7f5ec58 |
| institution | OA Journals |
| issn | 1438-8871 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj-art-33f758932a3e46dfbd8b2e52a7f5ec582025-08-20T01:52:40ZengJMIR PublicationsJournal of Medical Internet Research1438-88712025-05-0127e6875010.2196/68750Exchange of Quantitative Computed Tomography Assessed Body Composition Data Using Fast Healthcare Interoperability Resources as a Necessary Step Toward Interoperable Integration of Opportunistic Screening Into Clinical Practice: Methodological Development StudyYutong Wenhttps://orcid.org/0009-0003-8557-6665Vin Yeang Choohttps://orcid.org/0009-0004-3031-7567Jan Horst Eilhttps://orcid.org/0009-0005-9879-5722Sylvia Thunhttps://orcid.org/0000-0002-3346-6806Daniel Pinto dos Santoshttps://orcid.org/0000-0003-4785-6394Johannes Kasthttps://orcid.org/0009-0007-8510-6072Stefan Siglehttps://orcid.org/0000-0001-7475-4583Hans-Ulrich Prokoschhttps://orcid.org/0000-0001-6200-753XDiana Lizzhaid Ovelgönnehttps://orcid.org/0009-0001-5411-2548Katarzyna Boryshttps://orcid.org/0000-0001-6987-6041Judith Kohnkehttps://orcid.org/0009-0009-8826-3481Kamyar Arzidehhttps://orcid.org/0009-0005-6074-804XPhilipp Winnekenshttps://orcid.org/0009-0003-1625-3459Giulia Baldinihttps://orcid.org/0000-0002-5929-0271Cynthia Sabrina Schmidthttps://orcid.org/0000-0003-1994-0687Johannes Hauboldhttps://orcid.org/0000-0003-4843-5911Felix Nensahttps://orcid.org/0000-0002-5811-7100Obioma Pelkahttps://orcid.org/0000-0001-5156-4429René Hoschhttps://orcid.org/0000-0003-1760-2342 BackgroundFast Healthcare Interoperability Resources (FHIR) is a widely used standard for storing and exchanging health care data. At the same time, image-based artificial intelligence (AI) models for quantifying relevant body structures and organs from routine computed tomography (CT)/magnetic resonance imaging scans have emerged. The missing link, simultaneously a needed step in advancing personalized medicine, is the incorporation of measurements delivered by AI models into an interoperable and standardized format. Incorporating image-based measurements and biomarkers into FHIR profiles can standardize data exchange, enabling timely, personalized treatment decisions and improving the precision and efficiency of patient care. ObjectiveThis study aims to present the synergistic incorporation of CT-derived body organ and composition measurements with FHIR, delineating an initial paradigm for storing image-based biomarkers. MethodsThis study integrated the results of the Body and Organ Analysis (BOA) model into FHIR profiles to enhance the interoperability of image-based biomarkers in radiology. The BOA model was selected as an exemplary AI model due to its ability to provide detailed body composition and organ measurements from CT scans. The FHIR profiles were developed based on 2 primary observation types: Body Composition Analysis (BCA Observation) for quantitative body composition metrics and Body Structure Observation for organ measurements. These profiles were structured to interoperate with a specially designed Diagnostic Report profile, which references the associated Imaging Study, ensuring a standardized linkage between image data and derived biomarkers. To ensure interoperability, all labels were mapped to SNOMED CT (Systematized Nomenclature of Medicine – Clinical Terms) or RadLex terminologies using specific value sets. The profiles were developed using FHIR Shorthand (FSH) and SUSHI, enabling efficient definition and implementation guide generation, ensuring consistency and maintainability. ResultsIn this study, 4 BOA profiles, namely, Body Composition Analysis Observation, Body Structure Volume Observation, Diagnostic Report, and Imaging Study, have been presented. These FHIR profiles, which cover 104 anatomical landmarks, 8 body regions, and 8 tissues, enable the interoperable usage of the results of AI segmentation models, providing a direct link between image studies, series, and measurements. ConclusionsThe BOA profiles provide a foundational framework for integrating AI-derived imaging biomarkers into FHIR, bridging the gap between advanced imaging analytics and standardized health care data exchange. By enabling structured, interoperable representation of body composition and organ measurements, these profiles facilitate seamless integration into clinical and research workflows, supporting improved data accessibility and interoperability. Their adaptability allows for extension to other imaging modalities and AI models, fostering a more standardized and scalable approach to using imaging biomarkers in precision medicine. This work represents a step toward enhancing the integration of AI-driven insights into digital health ecosystems, ultimately contributing to more data-driven, personalized, and efficient patient care.https://www.jmir.org/2025/1/e68750 |
| spellingShingle | Yutong Wen Vin Yeang Choo Jan Horst Eil Sylvia Thun Daniel Pinto dos Santos Johannes Kast Stefan Sigle Hans-Ulrich Prokosch Diana Lizzhaid Ovelgönne Katarzyna Borys Judith Kohnke Kamyar Arzideh Philipp Winnekens Giulia Baldini Cynthia Sabrina Schmidt Johannes Haubold Felix Nensa Obioma Pelka René Hosch Exchange of Quantitative Computed Tomography Assessed Body Composition Data Using Fast Healthcare Interoperability Resources as a Necessary Step Toward Interoperable Integration of Opportunistic Screening Into Clinical Practice: Methodological Development Study Journal of Medical Internet Research |
| title | Exchange of Quantitative Computed Tomography Assessed Body Composition Data Using Fast Healthcare Interoperability Resources as a Necessary Step Toward Interoperable Integration of Opportunistic Screening Into Clinical Practice: Methodological Development Study |
| title_full | Exchange of Quantitative Computed Tomography Assessed Body Composition Data Using Fast Healthcare Interoperability Resources as a Necessary Step Toward Interoperable Integration of Opportunistic Screening Into Clinical Practice: Methodological Development Study |
| title_fullStr | Exchange of Quantitative Computed Tomography Assessed Body Composition Data Using Fast Healthcare Interoperability Resources as a Necessary Step Toward Interoperable Integration of Opportunistic Screening Into Clinical Practice: Methodological Development Study |
| title_full_unstemmed | Exchange of Quantitative Computed Tomography Assessed Body Composition Data Using Fast Healthcare Interoperability Resources as a Necessary Step Toward Interoperable Integration of Opportunistic Screening Into Clinical Practice: Methodological Development Study |
| title_short | Exchange of Quantitative Computed Tomography Assessed Body Composition Data Using Fast Healthcare Interoperability Resources as a Necessary Step Toward Interoperable Integration of Opportunistic Screening Into Clinical Practice: Methodological Development Study |
| title_sort | exchange of quantitative computed tomography assessed body composition data using fast healthcare interoperability resources as a necessary step toward interoperable integration of opportunistic screening into clinical practice methodological development study |
| url | https://www.jmir.org/2025/1/e68750 |
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