m+Medelevator.RU
← Поиск публикаций
BMJ open · 2026

Speech-based relapse prediction in psychosis using explainable AI: Protocol for the international multicentre observational TRUSTING study.

Протокол клинического исследованияСтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…

История · было → стало

Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.

Hüppi Roya Melanie, Bautista Lucía, Cecere Giacomo, Omlor Wolfgang, Just Sandra Anna, Koops Sanne, Hussain Musarrat, Tedeschi Enrico, Benke-Bruderer Stephan, Bora Emre, Lyne John, Kaiser Stefan, Sprüngli-Toffel Elodie, Kirschner Matthias, Mikalsen Karl Øyvind, Bongo Lars Ailo, Van der Eycken Erik, Španiel Filip, Elvevåg Brita, Sommer Iris Ec, Hinzen Wolfram, Homan Philipp

Аннотация

INTRODUCTION: The course of psychotic disorders typically involves relapses. Early warning signs vary between individuals and are difficult to detect in clinical practice, especially in outpatient settings. Speech provides a quantitative clinical marker for detecting such early warning signs. The EU Horizon project TRUSTING (A TRUSTworthy speech-based AI monitoring system for the prediction of relapse in individuals with schizophrenia) aims to develop and evaluate a speech-based monitoring system for predicting imminent psychotic relapses. The study will examine the potential for prospective relapse prediction, and feasibility and usability of the monitoring system.

METHODS AND ANALYSIS: In this multicentre observational study, n=240 remitted and at-risk-of-relapse adults with psychotic disorders and a comparison group with n=120 healthy participants (matched by age and sex) will be examined at six sites and in six different languages (German, French, Dutch, English, Czech and Turkish). The follow-up period is 6 months. The TRUSTING smartphone app will be used to collect weekly voice recordings through speech tasks; information on medication adherence, substance use, mood, anxiety and sleep quality; and motor data from a tapping task. Primary endpoints encompass model performance for relapse prediction, user adherence, transcription quality, usability of recordings and overall system usability. The primary analysis of user adherence, transcription quality, usability of recordings and overall system usability will be an unadjusted description of the respective proportions using 95% Wilson confidence intervals. Regarding relapse prediction, the predictive value of the risk estimates for relapse occurrence will be assessed using the area under the receiver operating characteristic curve. Exploratory analysis will be performed on potential speech-based markers associated with relapse risk.

ETHICS AND DISSEMINATION: This study has been approved by swissethics (Business Administration System for Ethics Committees number: 2025-01177). Findings from this project will be disseminated through peer-reviewed journal publications and presentations at relevant scientific conferences, as well as at public events related to mental health.

TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT07397975.

Предметные рубрики MeSH

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

Реквизиты и контакты