Full-scale indexing and semantic annotation of CT imaging: boosting FAIRness

TL;DR

This paper introduces a method to improve the organization and usability of CT imaging data using AI and standardized metadata for better healthcare applications.

Contribution

A novel approach for semantically enriching CT imaging data using TotalSegmentator and HL7 FHIR to enhance FAIRness.

Findings

Over 1.7 million CT image series were semantically enriched with SNOMED CT annotations.

Standardized metadata using HL7 FHIR improves data discoverability and interoperability.

Automated annotation methods need to evolve to handle growing clinical datasets.

Abstract

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.

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.

The study successfully integrates a robust process within the UKSH MeDIC, leading to the semantic enrichment of over 1.7 million CT image series and over 50 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.

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