ABSTRACT
ABSTRACT: 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.
ABSTRACT
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.
ABSTRACT
BACKGROUND: The discriminative utility of the neonatal sequential organ failure assessment (nSOFA) for early-onset sepsis (EOS) mortality in the neonatal intensive care unit (NICU) is unknown. OBJECTIVES: The objective of the study was to determine the utility of nSOFA for EOS mortality. METHODS: Multicenter, retrospective cohort study of NICU patients with EOS between 2012 and 2023. nSOFA scores of survivors and non-survivors were compared, and area under the receiver operating characteristics curve (AUROC) for mortality was calculated. RESULTS: 104 subjects were identified (88 lived, 16 died). AUROC at blood culture collection (T0), 6 h after collection (T6), and the maximum nSOFA at T0 or T6 (T0-6max) were 0.76 (95% CI: 0.62, 0.91), 0.89 (0.80, 0.99), and 0.87 (0.77, 0.97), respectively. Analyses restricted to birthweight (<1.5, <1 kg) or gestational age (<32, <29 week) cutoffs revealed AUROC ranges of 0.86-0.92 for T6 and 0.82-0.84 for T0-6max. CONCLUSIONS: The nSOFA showed good-to-excellent discrimination of mortality among infants with EOS in the NICU.
