Project description
Can speech analysis help prevent psychosis?
Psychotic disorders tend to have a waxing and waning course, with vulnerable persons being at risk for relapse. Psychotic relapses can often be prevented when a warning is received in time, so that appropriate measures can be taken. The EU-funded TRUSTING project is working under the hypothesis that spontaneous speech holds the key to predicting whether psychosis is imminent. The approach used will involve asking people at risk of psychotic relapse to record short speech samples at regular intervals. These samples will be analysed using artificial intelligence, with the aim of recognising changes that signal subtle psychotic symptoms. Our goal is to develop a user-friendly, trustworthy application that can be used from home to deliver a message when speech deviations predictive of psychotic relapse are detected.
Objective
Schizophrenia affects a staggering 21 million people worldwide, with 80% of these citizens suffering from a relapsing disease, putting their health and safety at enormous risk. Timely detection of these psychotic relapses would require very frequent contact with clinicians, which is neither desirable nor feasible. An accurate online relapse predictor could alert clinicians of subtle deterioration, which enables timely intervention and allow safe discontinuation of long-term medication, which so many affected citizens desire. Our Consortium demonstrated that subtle alterations in speech carry a predictive signal for psychosis onset. This project will develop an AI monitoring system that leverages spoken language processing (SLP) and natural language processing (NLP) of speech recorded at home to calculate the relapse risk. The monitoring tool we develop will be validated retrospectively in a longitudinal cohort, cross-sectionally, across six languages, after which it will be tested prospectively in a multicenter randomized trial, with the end goal of improving functional and clinical outcomes of those affected by schizophrenia. Developing such a system for exceptionally vulnerable people requires ‘buy-in’ from clinicians and mental health care service users, namely trust. A lack of trust is the biggest obstacle to the real-world implementation of a speech-based monitoring system. TRUSTING will develop a framework that systematically ensures addressing all the criteria for trustworthy AI put forward by the EU. This will ensure an empirically based and validated tool that can reliably detect pending relapse. As the core philosophy of trustworthiness is part of every aspect of the project, it will be a system more likely to be welcomed and embraced by service users and their carers. TRUSTING generates the scientific and social foundation for disruptive technology to deliver the unmet promise of an equitable and just form of healthcare for people at risk of relapse.
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HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinator
9713 GZ Groningen
Netherlands