Full-Scale Indexing and Semantic Annotation of CT Imaging: Boosting FAIRness

TL;DR

This paper presents a comprehensive method for indexing and semantically annotating large-scale CT imaging datasets to enhance their findability, interoperability, and reusability, thereby improving data FAIRness for AI applications.

Contribution

It introduces an automated process that semantically enriches CT images with SNOMED CT annotations and standardizes metadata with HL7 FHIR, enabling large-scale, interoperable medical imaging data integration.

Findings

Semantic enrichment of over 230,000 CT series

Integration of 8 million SNOMED CT annotations

Enhanced data discoverability and interoperability

Abstract

Background: The integration of artificial intelligence into medicine has led to significant advances, particularly in diagnostics and treatment planning. However, the reliability of AI models is highly dependent on the quality of the training data, especially in medical imaging, where varying patient data and evolving medical knowledge pose a challenge to the accuracy and generalizability of given datasets. Results: The proposed approach focuses on the integration and enhancement of clinical computed tomography (CT) image series for better findability, accessibility, interoperability, and reusability. Through an automated indexing process, CT image series are semantically enhanced using the TotalSegmentator framework for segmentation and resulting SNOMED CT annotations. The metadata is standardized with HL7 FHIR resources to enable efficient data recognition and data exchange between research projects. Conclusions: The study successfully integrates a robust process within the UKSH MeDIC, leading to the semantic enrichment of over 230,000 CT image series and over 8 million SNOMED CT annotations. The standardized representation using HL7 FHIR resources improves discoverability and facilitates interoperability, providing a foundation for the FAIRness of medical imaging data. However, developing automated annotation methods that can keep pace with growing clinical datasets remains a challenge to ensure continued progress in large-scale integration and indexing of medical imaging for advanced healthcare AI applications.