Development of Medical Imaging Data Standardization for Imaging-Based Observational Research : OMOP Common Data Model Extension
© 2024. The Author(s)..
The rapid growth of artificial intelligence (AI) and deep learning techniques require access to large inter-institutional cohorts of data to enable the development of robust models, e.g., targeting the identification of disease biomarkers and quantifying disease progression and treatment efficacy. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) has been designed to accommodate a harmonized representation of observational healthcare data. This study proposes the Medical Imaging CDM (MI-CDM) extension, adding two new tables and two vocabularies to the OMOP CDM to address the structural and semantic requirements to support imaging research. The tables provide the capabilities of linking DICOM data sources as well as tracking the provenance of imaging features derived from those images. The implementation of the extension enables phenotype definitions using imaging features and expanding standardized computable imaging biomarkers. This proposal offers a comprehensive and unified approach for conducting imaging research and outcome studies utilizing imaging features.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:37 |
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| Enthalten in: |
Journal of imaging informatics in medicine - 37(2024), 2 vom: 05. Apr., Seite 899-908 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Park, Woo Yeon [VerfasserIn] |
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| Links: |
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| Themen: |
Data collection [MeSH] |
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| Anmerkungen: |
Date Completed 22.04.2024 Date Revised 26.04.2024 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1007/s10278-024-00982-6 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM368046184 |
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| 520 | |a © 2024. The Author(s). | ||
| 520 | |a The rapid growth of artificial intelligence (AI) and deep learning techniques require access to large inter-institutional cohorts of data to enable the development of robust models, e.g., targeting the identification of disease biomarkers and quantifying disease progression and treatment efficacy. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) has been designed to accommodate a harmonized representation of observational healthcare data. This study proposes the Medical Imaging CDM (MI-CDM) extension, adding two new tables and two vocabularies to the OMOP CDM to address the structural and semantic requirements to support imaging research. The tables provide the capabilities of linking DICOM data sources as well as tracking the provenance of imaging features derived from those images. The implementation of the extension enables phenotype definitions using imaging features and expanding standardized computable imaging biomarkers. This proposal offers a comprehensive and unified approach for conducting imaging research and outcome studies utilizing imaging features | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Data collection [MeSH] | |
| 650 | 4 | |a Data integration | |
| 650 | 4 | |a Data standardization | |
| 650 | 4 | |a Multimodal data analysis | |
| 650 | 4 | |a Observational research | |
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| 700 | 1 | |a Alkasab, Tarik |e verfasserin |4 aut | |
| 700 | 1 | |a Dewey, Blake E |e verfasserin |4 aut | |
| 700 | 1 | |a You, Seng Chan |e verfasserin |4 aut | |
| 700 | 1 | |a Nagy, Paul |e verfasserin |4 aut | |
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