Imagine if a doctor could scan a lung CT image and instantly get a precise map of the disease instead of spending an hour measuring it by hand. This tool acts like a high-speed digital assistant that spots patterns in lung scans that are hard for humans to quantify. It turns complex images into a simple, structured report so doctors can pick the exact right treatment for each patient.
Lung doctors struggle to personalize treatments because analyzing chest CT scans is manual, time-consuming, and prone to human error. This leads to sub-optimal patient care and high economic burdens on healthcare systems.
An end-to-end clinical decision support system (CDSS) called LungQ-Care that automates the quantification of medical imaging and disease phenotypes.
If you are a hospital dealing with long diagnostic backlogs and manual CT analysis, this project developed LungQ-Care that reduces analysis time from 15-40 minutes to under 5 minutes per scan.
If you are a radiology clinic dealing with high variability in manual reporting, this project developed an AI system that provides structured, quantified biomarkers to ensure consistent and accurate reports.
If you are a pharma company dealing with the need for precise patient stratification in COPD trials, this project developed a system that quantifies disease phenotypes to enable personalized treatment plans.
Based on available project data, the system reduces costs by enabling personalized treatments and optimizing disease management through more efficient diagnostics.
Yes, Thirona aims to create a scalable AI platform that integrates seamlessly into existing hospital infrastructure.
Based on available project data, the technology is developed by Thirona BV, but specific licensing terms are not provided.
The system is designed to integrate into existing hospital infrastructure, converting CT scans into structured reports for lung doctors.
The project period runs from 2023-10-01 to 2025-09-30.
The project is led by a single entity, Thirona BV, a Dutch SME. With a 100% industry ratio and no university or research partners, the project is heavily focused on commercial application and rapid market deployment rather than basic research.
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