ArticleJournal of imaging informatics in medicine2026
Advancing the FAIRness of Multimodal Imaging Research Through the OMOP MI-CDM Framework: A Case Replication Study in Alzheimer's Disease.
Article in Journal of imaging informatics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The objective of this study is to demonstrate an end-to-end approach for operationalizing the Findable, Accessible, Interoperable, and Reusable (FAIR) principles in multimodal medical imaging research using standardized data models and reproducible computational workflows, illustrated by reproducing the design and directional findings of a published Alzheimer's disease (AD) imaging study. Clinical and imaging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI-4) were harmonized within the Observational Medical Outcomes Partnership Common Data Model and its Medical Imaging extension (OMOP MI-CDM). MRI acquisition metadata (DICOM) were extracted and mapped to standardized concepts, while clinical variables were integrated via reproducible extract-transform-load processes. Interoperable phenotypes were defined using OHDSI tools. Hippocampal volumes were derived from T1-weighted MRI using a fully automated machine learning segmentation pipeline (OpenMap-T1). Imaging attributes and derived measurements were stored as structured, provenance-preserving records in OMOP MI-CDM. We replicated a reference study evaluating hippocampal volume differences across AD, mild cognitive impairment (MCI), and cognitively normal controls, stratified by age and sex. We included 289 participants and 545 MRI studies. Across age- and sex-stratified cohorts, mean hippocampal volumes showed consistent directional reductions in AD compared with controls, with intermediate values in MCI, matching trends reported in the replication study. FAIR principles can be operationalized across the full imaging research pipeline using OMOP MI-CDM and automated analysis workflows. This framework enables transparent cohort definition, reproducible image processing, and interoperable reuse of machine learning-derived imaging features to support scalable validation and reproducible multimodal imaging research.
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