University Hospitals Leuven (UZ Leuven), a leading European medical center from Belgium, has initiated a pilot with contextflow to integrate AI into its radiology department. This 12-month project, supported by an InnoMatch grant, will focus on applying contextflow’s ADVANCE Chest CT solution to improve the diagnostic process for common lung conditions.
Addressing a Practical Need in Chest CT Imaging
With a substantial volume of over 10,000 chest CT scans performed annually, UZ Leuven faces the common challenge of ensuring high diagnostic accuracy while maintaining workflow efficiency. This project aims to address operational realities by providing radiologists with a robust AI chest aalysis tool designed for real-world clinical use as presented in Leuven.
The collaboration will deploy contextflow ADVANCE Chest CT as a comprehensive support for radiologists in chest CT analysis:
- – Detect and quantify pulmonary nodules, providing objective data for analysis.
- – Analyze lung tissue patterns, aiding in the assessment of interstitial lung disease (ILD).
- – Streamline the tracking of nodules over time for consistent patient monitoring.
- – Identify incidental findings, such as pulmonary embolisms, that may otherwise be missed.
Validating AI for Improved Patient Outcomes and Workflow
The primary objective of this project is to validate the clinical utility of AI radiology in a high-volume hospital setting. Over the next year, the teams at UZ Leuven and contextflow will work to measure the software’s impact on two key areas:
– Diagnostic Accuracy: The project will evaluate how the AI-driven insights contribute to the early and accurate detection of lung pathologies like cancer, ILD, and COPD. The goal is to provide data that supports earlier intervention and improves patient treatment pathways.
– Workflow Efficiency: A key performance indicator will be the tool’s effect on the radiology workflow. By automating time-consuming measurements and providing structured information, the software is expected to reduce report reading times and increase overall departmental productivity.
This project represents a valuable opportunity to generate solid evidence on the role of AI imaging in modern medicine. The findings will provide important insights for the healthcare institution and their diagnostic workflows. The results are intended to be compiled into a comprehensive report to benefit the broader medical community.