In Europe, 61 million people suffer from diabetes, and globally, this number reaches more than 500 million. Diabetes causes almost 7 million deaths annually and can lead to severe complications like blindness, kidney failure, and heart disease. Managing diabetes relies heavily on continuous glucose monitoring to guide insulin application and diet. However, current monitoring methods are limited, as they do not provide information about long-term disease progression or the effects of treatments.

The MOSAIC project aims to address this by developing a portable sensor that non-invasively monitors diabetes through the skin. The sensor’s data analytics convert images into readings, allowing users to receive quantitative information about their disease status without inspecting images themselves. By providing a new method for monitoring diabetes progression over time, rather than daily management, it can detect early signs of complications, enabling timely interventions such as medication or lifestyle changes. Additionally, the sensor can help schedule follow-up appointments based on changes in biomarkers, potentially improving patient outcomes, reducing healthcare costs, and decreasing mortality.

The objectives of the MOSAIC project are:

1. Miniaturize optoacoustic mesoscopy to create a portable sensor for non-invasive home or point-of-care measurements;
2. Use optoacoustics to monitor a wide range of diabetes biomarkers efficiently;
3. Employ explainable Artificial Intelligence (xAI) to implicitly analyze and investigate the relevance and predictive power of the optoacoustic biomarkers with and without additional clinical information on disease/vascular dysfunction;
4. Perform Proof-of-Concept study in animals and humans, examining the multivariate sensor performance and its ability to detect biomarkers of diabetes in a pre-clinical and clinical setting.