Imagine a health check-up that doesn't just look at whether you smoke, but 'smells' your breath and 'feels' your skin for hidden warning signs. This project combines wearable sensors and AI to find lung cancer risk factors in people who might never have touched a cigarette. It's like giving doctors a high-tech magnifying glass to spot danger long before a traditional scan would.
Current lung cancer screening relies on expensive CT scans that often lead to overdiagnosis and ignore non-smoking risk factors. There is a critical need for non-invasive, low-cost tools to identify high-risk individuals before they require imaging.
A multimodal toolbox consisting of a breath analyser (BAN), a skin-based sensing patch (WBSP), spectrometry devices (SPOC), and AI models integrated via an OMOP-aligned data platform.
If you are a wearable health tech manufacturer dealing with the need for clinical-grade diagnostic sensors — this project developed a skin-based sensing patch (WBSP) and breath analyser (BAN) that provide non-invasive biological data for cancer risk assessment.
If you are an AI diagnostic software provider dealing with fragmented clinical data — this project developed multimodal AI models integrating imaging, molecular, and sensor data to improve risk prediction accuracy.
If you are a private screening clinic dealing with the high cost and overdiagnosis of low-dose CT scans — this project developed a validated toolbox for precision screening of high-risk populations to optimize patient triage.
Based on available project data, specific pricing or unit costs for the toolbox components are not provided.
The project has already transitioned to large-scale clinical data generation across multiple European sites, suggesting a path toward industrial scalability.
Based on available project data, the specific licensing terms for the breath analyser and skin-based patches are not disclosed.
The project uses a Health Data Platform aligned with the OMOP Common Data Model to ensure harmonised multi-site data integration.
The project period runs from 2023-01-01 to 2026-12-31, indicating that final validation is ongoing until the end of 2026.
The consortium is highly balanced for commercial translation, featuring 24 partners across 9 countries. With 8 industry partners (including 7 SMEs), the industry ratio is 33%, which is high for a research-heavy project. This mix of 5 universities and 8 research institutions ensures a strong pipeline from molecular biology to market-ready sensing hardware.
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