A Multi-Model, Pixel-Native Framework for Automated Computed Tomography Series Labeling and Characterization: Proof-of-Concept Study.

backgroundAlthough Digital Imaging and Communications in Medicine (DICOM) metadata are widely used to manage medical imaging data and support clinical workflows, their suitability as a sole basis for automatic computed tomography (CT) series labeling and characterization is limited. DICOM metadata are frequently inconsistently populated, institution specific, use unregulated private tags, and have variable reliability even within standardized fields. Consequently, automated series selection for downstream AI applications often remains unreliable, necessitating manual curation within clinical workflows.

objectiveThis study presents Orchestrate, a modular AI framework for automated orchestration of CT imaging data. By integrating a hierarchy of deep learning models, Orchestrate enables pixel-level classification and routing of CT series and accurate metadata-independent identification of anatomical regions, contrast-enhanced series, and reconstruction kernels, supporting seamless downstream AI integration without manual curation.

methodsOrchestrate combined 3 pretrained models for anatomical regions, landmarks, and body part classification and 4 newly developed You Only Look Once (YOLO) v8-based models to classify contrast enhancement, recognize reconstruction kernels, and infer laterality. Three datasets were used: an internal development dataset comprising 27,418 CT studies for individual model development, an internal framework evaluation dataset comprising 200 CT studies to assess the complete framework under a simulated real-world scenario, and an external dataset from The Cancer Imaging Archive comprising 100 CT studies. For the internal development data, reference standards were derived from complete and unambiguous DICOM metadata following institutional definitions. For the 2 evaluation datasets, 3 radiographers independently established reference standards as DICOM metadata were not assumed to be complete or consistent. Clinical utility was assessed through cohort selection tasks involving 3 predefined target cases, with 3 radiologists reviewing selection accuracy. The interrater agreement was assessed using the Fleiss κ. The model performance was evaluated using F

resultsDICOM metadata were incomplete in the internal framework evaluation dataset and external dataset, with missing rates of 42.9% (413/963) and 92.9% (105/113) for contrast enhancement, respectively, and a missing reconstruction kernel information rate of 0.4% (4/963) in the internal framework evaluation dataset. During model development, individual models achieved macro-F

conclusionsOrchestrate enables automated pixel-based classification, detection, and semantic description of CT series, reducing reliance on manual selection and the risk of inconsistent metadata. By generating standardized semantic content, the framework provides a proof of concept for improving interoperability with clinical systems and supports the reliable, reproducible integration of AI-driven imaging pipelines into clinical workflows.