The objective of the algorithms is therefore to identify recurring patterns and assign meaning to them.<\/p>\n\n
Learning from Real-World Data<\/h2>\n\n
To identify these signatures, the models must be trained using data collected during the clinical study.<\/p>\n\n
One distinctive feature of the ALCOVE project is the simultaneous use of two types of data: on one hand, the signals generated by the electronic nose; on the other, the molecules present in exhaled breath identified through reference analytical techniques.<\/p>\n\n
This dual perspective makes it possible to observe the same phenomenon from two different angles. It provides a deeper understanding of what the device actually measures and allows researchers to assess both its performance and its limitations. <\/p>\n\n
Black Box vs. White Box: Making AI Understandable<\/h2>\n\n
Not all artificial intelligence models offer the same level of transparency.<\/p>\n\n
Some so-called\u201cblack box\u201d models can achieve excellent performance without explaining how they arrive at their conclusions. A simple analogy illustrates this: a dog trained to detect a disease may be highly effective, but it is impossible to know exactly what drives its decision. <\/p>\n\n
By contrast,\u201cwhite box\u201d models make it possible to trace the reasoning behind a result. They reveal the factors that lead to a decision by identifying, for example, which sensors or signal combinations are most influential. <\/p>\n\n
In ALCOVE, this focus on interpretability <\/strong>is a central principle.<\/p>\n\n This approach is fully aligned with European expectations, particularly under the AI Act,<\/strong> which encourages the development of transparent and explainable AI systems.<\/p>\n\n In healthcare, this requirement is even more critical. Understanding how results are obtained helps build confidence among healthcare professionals and can also advance scientific knowledge by shedding light on underlying biological mechanisms. <\/p>\n\n In this context, an algorithm does not simply provide an answer. It contributes to scientific understanding.<\/p>\n\n To develop these models, researchers also rely on Automated Machine Learning (AutoML) techniques.<\/strong><\/p>\n\n These approaches enable the system to automatically test different configurations, adjust parameters, and prepare data in the most effective way.<\/p>\n\n This efficiency gain allows researchers to focus on what matters most: analyzing results, interpreting findings, and collaborating with the other partners involved in the project.<\/p>\n\n Within ALCOVE, algorithms never work in isolation. Every result is discussed, challenged, and refined. This collaborative approach is essential to ensure that identified profiles are meaningful and to avoid misinterpretation. <\/p>\n\n The ultimate goal is to identify reliable signatures associated with lung cancer and understand how they are reflected in the collected data.<\/p>\n\n Algorithms therefore act as a bridge between technology and medicine, transforming complex signals into interpretable information.<\/p>\n\n Beyond technical performance, it is this ability to explain, understand, and share knowledge that represents one of the strengths of the ALCO<\/strong>VE project and a key condition for developing meaningful healthcare innovations.<\/p>\n\n In the ALCOVE project, everything starts with a simple action: breathing.Yet behind this apparent simplicity lies a major scientific challenge: transforming invisible signals into meaningful information for the early detection of lung cancer. While the electronic nose can detect extremely subtle variations in exhaled breath, these signals alone do not tell a story. They first […]<\/p>\n","protected":false},"author":6,"featured_media":2045,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"parent_blog_id":0,"footnotes":""},"categories":[106],"tags":[115],"sector":[100,111],"class_list":["post-2052","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-newsletter","tag-newsletter","sector-soins-de-sante-en","sector-technology"],"acf":[],"yoast_head":"\n![\"\"](\"https:\/\/alcove.crosss3.eu\/wp-content\/uploads\/sites\/2\/2026\/07\/tutorial-1024x531.png\")
<\/figure>\n\nA European Requirement: Explaining Decisions<\/h2>\n\n
AutoML : optimiser automatiquement les mod\u00e8les<\/h2>\n\n
Collective Intelligence Driving Analysis<\/h2>\n\n
Giving Meaning to Improve Detection<\/h2>\n\n