Facilitating personalised Lung Treatment Decisions through a Deeptech AI Clinical Decision Support System

Informazioni relative al progetto

LungQ-Care

ID dell’accordo di sovvenzione: 190100321

DOI

10.3030/190100321

Progetto chiuso

Data della firma CE 31 Ottobre 2023

Data di avvio 1 Ottobre 2023

Data di completamento 30 Settembre 2025

Finanziato da

• The European Innovation Council (EIC)

Costo totale

€ 4 756 250,00

Contributo UE

€ 2 499 999,00

Coordinato da

THIRONA BV
Netherlands

Periodic Reporting for period 2 - LungQ-Care (Facilitating personalised Lung Treatment Decisions through a Deeptech AI Clinical Decision Support System)

Periodo di rendicontazione: 2024-10-01 al 2025-09-30

Sintesi del contesto e degli obiettivi generali del progetto

The escalating global burden of respiratory diseases, highlighted by the World Health Organization as the third leading cause of death and disability, underscores an urgent need for innovative healthcare solutions. Chronic Obstructive Pulmonary Disease (COPD) and other pulmonary conditions continue to challenge healthcare systems, with a demand for precise and efficient diagnostic tools that far exceeds the current capacity. The advent of artificial intelligence (AI) in medical imaging presents a transformative opportunity to address these challenges. THOR, developed by Thirona, is positioned to revolutionize the lung care workflow through its advanced AI-driven clinical decision support system (CDSS).

Strategic Imperative:
Respiratory diseases not only degrade the quality of life, but also impose significant economic burdens on healthcare systems worldwide. Current diagnostic processes, particularly in imaging like chest CT scans, are hampered by time-consuming manual analyses prone to variability. This often leads to non-personalized and sub-optimal treatment plans. The introduction of THOR aims to disrupt this status quo by automating the analysis of chest CT scans, delivering rapid, accurate, and personalized medical reports to lung doctors, thereby facilitating tailored treatment plans.

Objectives of the Project:
THOR is designed to integrate seamlessly into existing hospital infrastructure. The primary objectives of THOR are to:
1. Provide lung doctors with structured, quantified reports that include disease-specific biomarkers and visual data, enabling personalized patient care.
2. Enhance the accuracy of these analyses by ensuring robustness against factors such as variation induced by disease manifestation, demographics, and scanners variability.
3. Reduce the time required for radiologists to analyse chest CT scans from an average of 15-40 minutes to under 5 minutes using THOR.
These objectives address the critical needs within the lung care value chain by significantly improving the efficiency and effectiveness of medical diagnostics and treatment planning.

Scale and Significance of Impact:
The implementation of THOR is expected to have substantial impacts across multiple dimensions:
• Clinical Efficiency: By reducing analysis time and improving report accuracy, THOR will allow for more patients to be seen and accurately diagnosed, thus potentially improving patients' quality of life through timely and appropriate treatment interventions.
• Economic Benefits: More efficient diagnostic processes will reduce the operational costs for healthcare providers and potentially decrease the financial burden on the healthcare system through more effective management of respiratory diseases.
• Strategic Healthcare Innovation: By setting a new standard for the integration of AI in clinical workflows, THOR aims to lead a shift towards more data-driven, precise, and patient-centered healthcare.

The deployment of THOR represents a strategic innovation in the management of respiratory diseases, particularly at a time when global healthcare systems face unprecedented pressures. Its ability to integrate advanced AI technology within the existing hospital workflows promises not only to enhance the efficiency of medical personnel but also to elevate the standard of care provided to patients suffering from debilitating lung diseases. As such, THOR is not merely a technological advancement; it is a necessary evolution in the continuing story of healthcare modernization, with far-reaching implications for patients, providers, and the broader medical community.

Lavoro eseguito dall’inizio del progetto fino alla fine del periodo coperto dalla relazione e principali risultati finora ottenuti

Over the course of the project, THOR evolved to a clinically validated, market-ready AI-based Clinical Decision Support System (CDSS) for thoracic CT imaging. Work focused on three pillars: development of a robust AI model suite, creation of scalable infrastructure for clinical integration, and validation through real-world pilot studies.

The AI model suite was enhanced to deliver comprehensive quantification for major lung diseases, including COPD, bronchiectasis, cystic fibrosis, and asthma. Early prototypes showed promising results, and subsequent iterations incorporated advanced segmentation algorithms for airways, lobes, and fissures, plus mucus detection and ventilation estimation. These improvements were supported by expanded training datasets to ensure robustness. A dedicated quality control system safeguarded reliability, integrating artifact detection and confidence scoring. By the end of the project, the models achieved strong performance metrics within clinical usability thresholds.

Parallel to algorithmic development, significant effort went into building infrastructure integrating into hospital workflows. A modular containerised architecture guaranteed portability, scalability, and simplified updates. A cloud-based platform orchestrated data transfer, automated processing, and generated structured reports, while integration endpoints were developed to support both cloud-to-cloud and on-premise deployments. Multiple report formats were finalised, including DICOM encapsulated PDFs and structured reports, ensuring compatibility with diverse clinical environments.

Pilot studies in major hospitals validated the system’s technical performance and usability. Clinicians confirmed THOR’s outputs were accurate, interpretable, and aligned with workflow requirements. Feedback informed refinements in report design and integration processes, resulting in a solution that maximises clinical relevance. Security and compliance assessments confirmed adherence to GDPR and ISO 27001 standards, while preparatory steps for CE-marking were completed. By the end of the reporting period, THOR had reached Technology Readiness Level 8, positioning it for imminent commercial deployment.

Progressi oltre lo stato dell’arte e potenziale impatto previsto (incluso l’impatto socioeconomico e le implicazioni sociali più ampie del progetto fino ad ora)

The project delivered a transformative solution addressing inefficiencies in thoracic imaging workflows. THOR transitioned from a fragmented AI imaging tool into a fully integrated CDSS supporting personalised lung treatment decisions. The platform combines automated quantification, structured reporting, and seamless interoperability with hospital IT systems, enabling clinicians to move from subjective interpretation toward data-driven, standardised care.

The anticipated impact spans clinical, economic, and societal dimensions. Clinically, THOR enhances diagnostic precision and consistency, reducing variability and improving treatment planning for chronic lung diseases. Economic evaluations through Health Technology Assessment indicate substantial cost savings in COPD care pathways. Societal benefits include measurable improvements in quality of life, with gains approaching one QALY over five years for severe COPD patients and potential life extension in advanced stages.

To ensure widespread uptake, several key needs have been identified. Continued real-world evidence generation and multi-centre demonstrations will strengthen clinical confidence. Strategic partnerships will accelerate integration and adoption. Regulatory alignment for CE-marking and FDA clearance remains essential, alongside supportive frameworks for AI in healthcare. Access to scale-up financing and tailored commercialisation strategies will be critical to penetrate diverse markets effectively. Finally, ongoing investment in interoperability and cybersecurity will safeguard compliance and trust as the platform expands internationally.