Developing Advanced AI Ecosystems to Enhance Diagnosis and Care for Patients with Depression

Area of Science:

• Neuroscience
• Digital Health
• Artificial Intelligence

Background:

• Major Depressive Disorder (MDD) significantly impairs daily functioning.
• Current depression treatments face challenges in efficacy and accessibility.

Purpose of the Study:

• To present a project developing innovative therapy systems for MDD.
• To improve depression treatment outcomes and accessibility through integrated digital solutions.

Main Methods:

• Developing a multimodal neurofeedback (NFB) system integrating electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS).
• Creating an AI-based virtual therapy assistant (VTA) for personalized patient support and data collection.
• Establishing an open-source NFB toolbox for EEG-fNIRS integration.

Main Results:

• The project aims to create an integrated system combining NFB and VTA for enhanced therapeutic efficacy.
• The VTA will collect behavioral data and offer personalized feedback.
• The open-source toolbox will facilitate broader adoption of integrated NFB.

Conclusions:

• This project seeks to advance depression treatment by merging digital therapy, AI, and mobile applications.
• The proposed systems have the potential to improve treatment outcomes and accessibility for individuals with MDD.