Imagine if we could predict a child's mental health struggles by looking at how their genes react to their environment, like a weather forecast for the brain. This work identifies the biological 'red flags' that appear as kids grow up. It then uses AI to spot these patterns early so doctors can step in before a crisis happens.
Current mental health diagnostics for youth are often reactive and inconsistent across regions. There is a lack of biological markers and predictive tools to identify at-risk youth before severe illness occurs.
An AI-powered proof-of-concept prediction instrument and a harmonized set of clinical assessment tools for youth aged 12-24.
If you are a mental health app developer dealing with low user retention due to generic tracking — this project developed AI-driven instruments for self-detection and monitoring that provide personalized mental health trajectory predictions for users aged 12-24 years.
If you are a drug discovery firm dealing with a lack of specific biological targets for youth psychiatry — this project developed a knowledge base of functional epigenomics that provides actionable biological targets for mental illness.
If you are a private clinic network dealing with inconsistent diagnostic methods across different sites — this project developed a validated set of behavioral and biological instruments harmonized across European clinical settings to ensure robust quantitative assessment.
Based on available project data, specific pricing or cost structures for the instruments are not provided; the project is funded by an EU contribution of EUR 8,107,980.
The project aims to harmonize instruments across European clinical settings and uses longitudinal general population datasets, suggesting a design intended for broad scale application.
Based on available project data, specific licensing terms are not mentioned, though the project involves a consortium of 19 partners including one industry SME.
The project provides clinician-empowering AI-driven instruments designed for early detection, prediction, and monitoring within clinical settings.
The project period runs from 2022-06-01 to 2027-05-31, with an aim to reduce mental suffering within the next 5-10 years.
The consortium is heavily academic, consisting of 15 universities and 2 research institutions, which ensures deep scientific rigor. However, the industrial presence is lean at only 5% (1 SME), suggesting the project is currently in the translation phase from lab to market rather than a commercial product launch.
Contact Universiteit Maastricht regarding the AI-powered proof-of-concept instrument.
Contact us to bridge the gap between these genomic markers and your product pipeline.
