Developing Advanced AI Ecosystems to Enhance Diagnosis and Care for Patients with Depression.
Franziska KleinFrerk Müller-Von AschwegePatrick ElfertJulien RäkerAlexandra PhilipsenNiclas BraunBenjamin SelaskowskiAnnika WiebeMatthias GuthJohannes SpallekSigrid SeussBenjamin StoreyLeo N GeppertIngo LückAndreas HeinPublished in: Studies in health technology and informatics (2023)
Major Depressive Disorder (MDD) has a significant impact on the daily lives of those affected. This concept paper presents a project that aims at addressing MDD challenges through innovative therapy systems. The project consists of two use cases: a multimodal neurofeedback (NFB) therapy and an AI-based virtual therapy assistant (VTA). The multimodal NFB integrates EEG and fNIRS to comprehensively assess brain function. The goal is to develop an open-source NFB toolbox for EEG-fNIRS integration, augmented by the VTA for optimized efficacy. The VTA will be able to collect behavioral data, provide personalized feedback and support MDD patients in their daily lives. This project aims to improve depression treatment by bringing together digital therapy, AI and mobile apps to potentially improve outcomes and accessibility for people living with depression.
Keyphrases
- major depressive disorder
- quality improvement
- bipolar disorder
- depressive symptoms
- end stage renal disease
- healthcare
- artificial intelligence
- resting state
- chronic kidney disease
- physical activity
- functional connectivity
- pain management
- sleep quality
- type diabetes
- climate change
- palliative care
- machine learning
- prognostic factors
- newly diagnosed
- metabolic syndrome
- mesenchymal stem cells
- bone marrow
- big data
- deep learning
- brain injury
- data analysis
- combination therapy