ERDF research project

ORAKEL

Better prediction of relapses in depressive disorders by detecting early warning signs using AI.

Deception Detection In virtual sales meetings

The ORAKEL project aims to use artificial intelligence (AI) to precisely identify early warning signs of depressive relapses, thus enabling timely intervention. By collecting multimodal data (video and audio recordings) and developing specific deep learning models, the project will analyse patients’ behavioural patterns and emotional states. These innovative technologies will support clinical use via a user-friendly graphical interface and help to treat depressive episodes at an early stage.

The focus is on improving relapse prevention, optimising psychiatric care and relieving the burden on medical staff by means of intelligent assistance systems. The project combines expertise in psychiatry and AI development to create personalised approaches to depression treatment and to advance research in key digital technologies.

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Deep Learning-Based Gaze Estimation - A Review
March 25, 2026
Deep Learning-Based Gaze Estimation: A Review
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Die ORAKEL-Studie Magdeburg - Rezidivfrüherkennung bei Depression durch KI-gestützte Audio-/Videoanalyse
March 01, 2026
Die ORAKEL-Studie Magdeburg - Rezidivfrüherkennung bei Depression durch KI-gestützte Audio-/Videoanalyse
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STCM-Mamba - Multimodal Spatio-Temporal Cross-Modal Mamba for Depression Detection
October 31, 2025
STCM-Mamba: Multimodal Spatio-Temporal Cross-Modal Mamba for Depression Detection
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Cross-Modal Fusion Mamba for Multimodal Depression Detection
October 29, 2025
Cross-Modal Fusion Mamba for Multimodal Depression Detection
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Multi-Modal AI-Based Pain Detection in Intermediate Care Patients in the Postoperative Phase
October 08, 2025
Multi-Modal AI-Based Pain Detection in Intermediate Care Patients in the Postoperative Phase
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Patient Data Recording Start
April 01, 2025
Data Collection at University Hospital for Psychiatry and Psychotherapy Begins