The healthcare system is becoming increasingly complex, with more medical specialties involved in diagnosis, treatment, and follow-up care of patients. However, the treatment options for mental illnesses, such as depression, remain inadequate. Depression is a highly underestimated psychiatric condition that causes immense individual and societal suffering, as well as significant economic burden. This project aims to explore innovative therapy systems for improving diagnostic, interactive, and personalized approaches for patients with depression, using AI-based algorithms.
The project focuses on two approaches: a multimodal neurofeedback system that uses feedback to activate self-regulatory mechanisms of the brain, and a virtual therapy assistant that continuously collects patient data to derive individualized treatment measures based on AI-based algorithms. Both approaches aim to provide continuous monitoring of the patient’s mental state and provide an individualized treatment plan. The project aims to reduce the enormous healthcare costs associated with depression and to alleviate individual suffering.